Changes ui updated

.dockerignore

@@ -0,0 +1,12 @@
+.env
+.env.*
+venv/
+seed/
+.venv/
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.git
+.gitignore
+app/service-account.json

.env

@@ -1,2 +1,5 @@
 GOOGLE_PROJECT_ID=sylvan-deck-387207
-GOOGLE_APPLICATION_CREDENTIALS=C:\Users\rithv\OneDrive\Desktop\decision_engine_project\service-account.json
+GOOGLE_APPLICATION_CREDENTIALS=C:\Users\rithv\OneDrive\Desktop\decision_engine_project\app\service-account.json
+DATABASE_URL = "postgresql://postgres:postgres@localhost:5432/decision_engine"
+GOOGLE_SEARCH_API_KEY = AIzaSyBrqZPVbX8-HukUQEccLXwJ3MF6ZbG5cwc
+GOOGLE_SEARCH_CX = 02d66a7fa55f7490c

.gitignore

@@ -1,5 +1,32 @@
+# Virtual environments
 venv/
 seed/
+.venv/
+
+# Environment & secrets
 .env
+.env.*
+app/service-account.json
+
+# Python cache
 __pycache__/
 *.pyc
+*.pyo
+*.pyd
+
+# Build artifacts
+dist/
+build/
+*.egg-info/
+
+# IDE
+.vscode/
+.idea/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Docker volumes
+pg_data/
+redis_data/

Dockerfile

@@ -0,0 +1,30 @@
+# Use Python 3.11 slim
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libpq-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first (for Docker layer caching)
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download spaCy model
+RUN python -m spacy download en_core_web_sm
+
+# Copy entire project
+COPY . .
+
+# Expose FastAPI port
+EXPOSE 8000
+
+# Start the server
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

app/analyze_logs.py

@@ -0,0 +1,64 @@
+import json
+from collections import defaultdict
+
+LOG_FILE = "logs/app.log"
+
+total_requests = 0
+total_latency = 0
+query_count = defaultdict(int)
+slow_requests = []
+
+with open(LOG_FILE, "r") as f:
+    for line in f:
+        try:
+            log = json.loads(line)
+
+            total_requests += 1
+            latency = log.get("latency_ms", 0)
+            query = log.get("query", "")
+
+            total_latency += latency
+            query_count[query] += 1
+
+            if latency > 1000:  # slow threshold
+                slow_requests.append((query, latency))
+
+        except:
+            continue
+
+# Results
+print("\n📊 BASIC METRICS")
+print("=" * 30)
+
+if total_requests > 0:
+    print(f"Total Requests: {total_requests}")
+    print(f"Avg Latency: {total_latency // total_requests} ms")
+
+print("\n🔥 Top Queries:")
+for q, count in sorted(query_count.items(), key=lambda x: x[1], reverse=True)[:5]:
+    print(f"{q} → {count} times")
+
+print("\n⚠️ Slow Requests (>1000ms):")
+for q, lat in slow_requests[:5]:
+    print(f"{q} → {lat} ms")
+
+cache_hits = 0
+
+if log.get("latency_ms", 0) < 500:
+    cache_hits += 1
+
+print(f"\n⚡ Fast Requests (<500ms): {cache_hits}")
+
+hit_count = 0
+
+if log.get("cache_status") in ["strong_hit", "refined_hit"]:
+    hit_count += 1
+
+hit_rate = (hit_count / total_requests) * 100
+
+fallback_count = 0
+
+if log.get("cache_status") == "miss":
+    fallback_count += 1
+
+fallback_rate = (fallback_count / total_requests) * 100

app/bootstrap.py

@@ -7,12 +7,11 @@ from sentence_transformers import SentenceTransformer
 import requests as http_requests
 import google.auth
 import google.auth.transport.requests
-import os
 from app.vertex_client import get_access_token
 
 load_dotenv()
 
-DATABASE_URL = "postgresql://postgres:postgres@localhost:5432/decision_engine"
+DATABASE_URL = os.getenv("DATABASE_URL")
 engine = create_engine(DATABASE_URL)
 model = SentenceTransformer("all-MiniLM-L6-v2")
 
@@ -42,31 +41,41 @@ def clean_json_response(raw: str) -> str:
     raise ValueError("No JSON object found in response")
 
 
-def validate_schema(data: dict) -> dict:
-    """
-    Validate and clean Gemini response.
-    - Only keep valid categories
-    - Only keep string attributes
-    - Remove empty categories
-    - Cap at 5 attributes per category
-    """
+def validate_schema(data: dict, query: str) -> dict:
     cleaned = {}
+    query_words = set(query.lower().split())
+    
+    # Define topic-aware category relevance
+    IRRELEVANT_COMBOS = {
+        "shoes": ["processor", "ram", "gpu", "cpu", "battery", "charging"],
+        "food": ["processor", "gpu", "engine", "torque", "horsepower"],
+        "college": ["torque", "engine", "gpu", "charging speed"],
+    }
+    
+    # Get blocked terms for this query
+    blocked = []
+    for topic, terms in IRRELEVANT_COMBOS.items():
+        if topic in query.lower():
+            blocked.extend(terms)
+
     for category, attributes in data.items():
-        # Normalize category name
         cat = category.strip().title()
-
-        # Skip invalid categories
         if cat not in VALID_CATEGORIES:
             print(f"Skipping unknown category: {cat}")
             continue
 
-        # Only keep string attributes
-        attrs = [a.strip() for a in attributes if isinstance(a, str) and a.strip()]
-
-        # Cap at 5 per category
-        attrs = attrs[:6]
+        attrs = []
+        for a in attributes:
+            if not isinstance(a, str) or not a.strip():
+                continue
+            # ✅ Reject attributes containing blocked terms
+            a_lower = a.lower()
+            if any(b in a_lower for b in blocked):
+                print(f"❌ Rejected irrelevant attribute: {a}")
+                continue
+            attrs.append(a.strip())
 
-        # Skip empty categories
+        attrs = attrs[:12]
         if attrs:
             cleaned[cat] = attrs
 
@@ -77,57 +86,75 @@ def validate_schema(data: dict) -> dict:
 
 
 def call_gemini(query: str) -> dict:
-    
-
     prompt = f"""You are a world-class decision analysis expert.
 
-Task: Generate the MOST IMPORTANT and FREQUENTLY USED evaluation criteria for someone making a decision about: "{query}"
+Task: Generate a COMPREHENSIVE list of evaluation criteria for: "{query}"
 
-Rules:
-- Only include criteria that are HIGHLY RELEVANT to "{query}"
-- Prioritize criteria that people MOST COMMONLY consider for this topic
-- Each attribute must be SPECIFIC and MEASURABLE, not generic
-- Order attributes by importance (most important first)
-- Use concise names (2-5 words max per attribute)
+MANDATORY RULES:
+- Select 6-8 categories from the allowed list
+- Generate EXACTLY 8-10 attributes per category — this is mandatory
+- Each attribute must be SPECIFIC and MEASURABLE for "{query}"
+- First 3 attributes in EVERY category must be the MOST SEARCHED specs
+- For tech products: always start with Processor, RAM, Battery, Display, Camera
+- For vehicles: always start with Engine, Mileage, Price, Safety
+- Order strictly by: most searched → most compared → most reviewed
+- Use concise names (2-5 words max)
+- DO NOT generate less than 8 attributes per category
 
-Output format: Pure JSON only. No markdown. No explanation.
-Use ONLY these category keys: Performance, Financial, Risk, Maintenance, Benefits, Time, Requirements, Scalability, Alternatives, Usability, Security, Reliability, Support, Sustainability
+Allowed category keys:
+Performance, Financial, Risk, Maintenance, Benefits, Time, Requirements, 
+Scalability, Alternatives, Usability, Security, Reliability, Support, Sustainability
 
-Example for "buying a car":
-{{"Performance":["Engine Power","Top Speed","0-100 Acceleration","Fuel Efficiency"],"Financial":["Purchase Price","Insurance Cost","Resale Value","Running Cost"],"Maintenance":["Service Interval","Spare Parts Availability","Warranty Period"]}}
+Example of CORRECT format with enough attributes:
+{{"Performance":["Engine Power","Torque Output","Top Speed","0-100 kmph Time","Fuel Efficiency","Gear Smoothness","Braking Distance","Tyre Grip","Suspension Quality","NVH Levels"],"Financial":["Ex-showroom Price","On-road Price","EMI Options","Insurance Cost","Fuel Cost Monthly","Resale Value","Maintenance Cost","Road Tax","Accessories Cost","Total Ownership Cost"],"Reliability":["Engine Reliability","Electrical Issues","Common Problems","Long Term Durability","Brand Track Record","Owner Satisfaction","Recall History","Service Quality","Spare Parts Life","Warranty Claims"]}}
 
 Now generate for: "{query}"
-Return ONLY the JSON object."""
+Return ONLY valid JSON. No markdown. No explanation. Minimum 8 attributes per category."""
 
     url = f"https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{LOCATION}/publishers/google/models/gemini-2.5-flash-lite:generateContent"
 
     for attempt in range(3):
         try:
-            res = http_requests.post(url, 
-            headers={
-                "Authorization": f"Bearer {get_access_token()}",
-                "Content-Type": "application/json"},
-            json={
-                "contents": [{"role" : "user","parts": [{"text": prompt}]}],
-                "generationConfig": {"temperature": 0.2, "maxOutputTokens": 1024}})
-
-            # Handle rate limit with exponential backoff
+            res = http_requests.post(
+                url,
+                headers={
+                    "Authorization": f"Bearer {get_access_token()}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                    "generationConfig": {
+                        "temperature": 0.1,  # lower = more accurate, less random
+                        "maxOutputTokens": 1024
+                    }
+                },
+                timeout=30
+            )
+
             if res.status_code == 429:
-                wait = 2 ** attempt  # 1s, 2s, 4s
-                print(f" Rate limited, waiting {wait}s... (attempt {attempt + 1})")
+                wait = 2 ** attempt
+                print(f"Rate limited, waiting {wait}s... (attempt {attempt + 1})")
                 time.sleep(wait)
                 continue
             
-            res.raise_for_status()
-            raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
+            print("Status Code:", res.status_code)
+            if res.status_code != 200:
+                print("❌ ERROR RESPONSE:")
+                print(res.text)
+                raise RuntimeError("Vertex API failed")
+
+            data_json = res.json()
+            print("✅ RAW RESPONSE:", str(data_json)[:500])
             
+            raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
             clean = clean_json_response(raw)
             data = json.loads(clean)
-            validated = validate_schema(data)
+            validated = validate_schema(data,query)
+            print(f"Gemini generated {sum(len(v) for v in validated.values())} attributes across {len(validated)} categories")
             return validated
 
         except (json.JSONDecodeError, ValueError) as e:
-            print(f" Attempt {attempt + 1} failed: {e}")
+            print(f"Attempt {attempt + 1} failed: {e}")
             if attempt == 2:
                 raise RuntimeError(f"Gemini failed after 3 attempts: {e}")
 
@@ -135,8 +162,6 @@ Return ONLY the JSON object."""
 
 
 def bootstrap_domain(query: str):
-    
-    # Retry up to 3 times if Gemini returns bad JSON
     data = None
     for attempt in range(3):
         try:
@@ -148,9 +173,32 @@ def bootstrap_domain(query: str):
             if attempt == 2:
                 raise RuntimeError(f"Gemini failed after 3 attempts: {e}")
 
-    with engine.begin() as conn:
-        domain_embedding = model.encode(query).tolist()
+    if not data:
+        raise RuntimeError("No data generated")
+
+    # ✅ Quality gate — reject low quality bootstraps
+    total_attrs = sum(len(v) for v in data.values())
+    if total_attrs < 10:
+        raise ValueError(f"Quality gate failed: only {total_attrs} attributes generated")
 
+    # ✅ Duplicate domain detection — check similarity before storing
+    model_local = SentenceTransformer("all-MiniLM-L6-v2")
+    domain_embedding = model_local.encode(query).tolist()
+
+    with engine.begin() as conn:
+        # Check if very similar domain already exists
+        existing = conn.execute(text("""
+            SELECT name, embedding <-> CAST(:emb AS vector) AS distance
+            FROM domains
+            ORDER BY distance
+            LIMIT 1
+        """), {"emb": str(domain_embedding)}).fetchone()
+
+        if existing and existing.distance < 0.15:
+            print(f"⚠️ Similar domain already exists: '{existing.name}' (distance: {existing.distance:.3f}) — skipping bootstrap")
+            return  # ✅ Don't store duplicate
+
+        # Store domain
         domain_id = conn.execute(text("""
             INSERT INTO domains (name, embedding)
             VALUES (:n, CAST(:e AS vector))
@@ -167,11 +215,11 @@ def bootstrap_domain(query: str):
             """), {"d": domain_id, "g": group}).scalar()
 
             for attr in attrs:
-                emb = model.encode(attr).tolist()
+                emb = model_local.encode(attr).tolist()
                 conn.execute(text("""
                     INSERT INTO attributes (group_id, name, embedding)
                     VALUES (:gid, :name, CAST(:emb AS vector))
                     ON CONFLICT (group_id, name) DO NOTHING
                 """), {"gid": group_id, "name": attr, "emb": str(emb)})
 
-    print(f"Domain bootstrapped: {query}")
+    print(f"✅ Domain bootstrapped: {query} ({total_attrs} attributes)")

app/check_db.py

@@ -1,10 +1,12 @@
 from sqlalchemy import create_engine, text
+import os
 try:
     from app.db_schema import ensure_schema
 except ModuleNotFoundError:
     from db_schema import ensure_schema
 
-engine = create_engine("postgresql://postgres:postgres@localhost:5432/decision_engine")
+DATABASE_URL = os.getenv("DATABASE_URL")
+engine = create_engine(DATABASE_URL)
 
 with engine.connect() as conn:
     ensure_schema(conn)

app/db_schema.py

@@ -1,9 +1,6 @@
 from sqlalchemy import text
 from sqlalchemy.exc import SQLAlchemyError
 
-
-
-
 def ensure_schema(engine) -> bool:
     with engine.connect().execution_options(isolation_level="AUTOCOMMIT") as conn:
         conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
@@ -42,4 +39,23 @@ def ensure_schema(engine) -> bool:
         """))
         conn.execute(text("ALTER TABLE attributes DROP CONSTRAINT IF EXISTS attributes_name_key"))
         
+        conn.execute(text("""
+        CREATE TABLE IF NOT EXISTS attribute_feedback (
+        id SERIAL PRIMARY KEY,
+        query TEXT NOT NULL,
+        attribute_name TEXT NOT NULL,
+        domain TEXT,
+        click_count INTEGER DEFAULT 1,
+        last_clicked TIMESTAMP DEFAULT NOW(),
+        UNIQUE(query, attribute_name)
+            )
+        """))
+        
+        conn.execute(text("""
+            ALTER TABLE domains 
+            ADD COLUMN IF NOT EXISTS quality_score FLOAT DEFAULT 1.0,
+            ADD COLUMN IF NOT EXISTS created_at TIMESTAMP DEFAULT NOW(),
+            ADD COLUMN IF NOT EXISTS query_count INTEGER DEFAULT 0
+        """))
+    
     return True

app/embedding_cache.py

@@ -11,7 +11,7 @@ def get_or_encode(query: str, model) -> list:
     otherwise encodes with model and caches it.
     Always call this with the NORMALIZED query.
     """
-    key = f"emb:{query}"
+    key = f"emb:v1:{query}"
 
     # Check Redis first
     cached = redis_client.get(key)

app/insert_test.py

@@ -0,0 +1,20 @@
+from sqlalchemy import create_engine, text
+from sentence_transformers import SentenceTransformer
+import os
+
+DATABASE_URL = os.getenv("DATABASE_URL")
+engine = create_engine(DATABASE_URL)
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+name = "Range"
+embedding = model.encode(name).tolist()
+
+with engine.connect() as conn:
+    conn.execute(text("""
+        INSERT INTO attributes (name, embedding)
+        VALUES (:name, :embedding)
+    """), {"name": name, "embedding": embedding})
+    conn.commit() 
+
+print("Inserted successfully")

app/log_cleanup.py

@@ -0,0 +1,15 @@
+import os
+import time
+
+LOG_FILE = "logs/app.log"
+MAX_SIZE_MB = 50  # rotate if too big
+
+def cleanup_logs():
+    if not os.path.exists(LOG_FILE):
+        return
+
+    size_mb = os.path.getsize(LOG_FILE) / (1024 * 1024)
+
+    if size_mb > MAX_SIZE_MB:
+        os.rename(LOG_FILE, f"logs/app_{int(time.time())}.log")
+        open(LOG_FILE, "w").close()

app/logger.py

@@ -0,0 +1,20 @@
+import os
+import json
+from datetime import datetime
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+LOG_DIR = os.path.join(BASE_DIR, "logs")
+LOG_FILE = os.path.join(LOG_DIR, "app.log")
+
+def log_event(data: dict):
+    try:
+        os.makedirs(LOG_DIR, exist_ok=True)
+
+        data["timestamp"] = datetime.utcnow().isoformat()
+
+        with open(LOG_FILE, "a") as f:
+            f.write(json.dumps(data) + "\n")
+
+    except Exception as e:
+        print(f"❌ Logging failed: {e}")
+        

app/main.py

@@ -15,7 +15,12 @@ import requests as http_requests
 import os
 import re
 import time
+import json as json_module
+import hashlib
+import redis as redis_lib
 from app.vertex_client import get_access_token
+from app.logger import log_event
+from fastapi import Request
 
 load_dotenv()
 
@@ -29,17 +34,37 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-DATABASE_URL = "postgresql://postgres:postgres@localhost:5432/decision_engine"
+DATABASE_URL = os.getenv("DATABASE_URL")
 engine = create_engine(DATABASE_URL)
 model = SentenceTransformer("all-MiniLM-L6-v2")
 
 GOOGLE_PROJECT_ID = "sylvan-deck-387207"
 LOCATION = "us-central1"
+GOOGLE_SEARCH_API_KEY = os.getenv("GOOGLE_SEARCH_API_KEY")
+GOOGLE_SEARCH_CX = os.getenv("GOOGLE_SEARCH_CX")
+
+# Gemini URL
+GEMINI_URL = f"https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{GOOGLE_PROJECT_ID}/locations/{LOCATION}/publishers/google/models/gemini-2.5-flash-lite:generateContent"
 
 # ── Startup ──────────────────────────────────────────
 ensure_schema(engine)
 create_index_if_not_exists()
-print("✅ System ready")
+print("System ready")
+
+
+# ── Redis helper ──────────────────────────────────────
+def get_redis():
+    try:
+        r = redis_lib.Redis(host='localhost', port=6379, decode_responses=True)
+        r.ping()
+        return r
+    except Exception:
+        return None
+
+def get_live_cache_key(query: str) -> str:
+    word_count = len(query.strip().split())
+    prefix = "live:full" if word_count >= 3 else "live:short"
+    return f"{prefix}:{hashlib.md5(query.lower().strip().encode()).hexdigest()}"
 
 
 # ── Async cache writer ────────────────────────────────
@@ -57,10 +82,9 @@ def is_gibberish(text: str) -> bool:
     words = text.lower().split()
     if not words:
         return True
-
     gibberish_count = 0
     for word in words:
-        if len(word) <= 3:  # allow short words: ev, ai, bmw, suv
+        if len(word) <= 3:
             continue
         if re.search(r'[^aeiou]{6,}', word):
             gibberish_count += 1
@@ -72,97 +96,654 @@ def is_gibberish(text: str) -> bool:
         if re.search(r'[a-z]\d{3,}[a-z]|[0-9]{4,}[a-z]', word):
             gibberish_count += 1
             continue
-
     return gibberish_count > len(words) / 2
 
 
+# ── Middleware ────────────────────────────────────────
+@app.middleware("http")
+async def log_requests(request, call_next):
+    start_time = time.time()
+    response = await call_next(request)
+    latency = int((time.time() - start_time) * 1000)
+    cache_status = getattr(request.state, "cache_status", "missing")
+
+    try:
+        query = request.query_params.get("query", "")
+        category = request.query_params.get("category", "general")
+        log_event({
+            "path": request.url.path,
+            "query": query,
+            "latency_ms": latency,
+            "status_code": response.status_code,
+            "category": category,
+            "cache_status": cache_status
+        })
+        print("CACHE:", cache_status)
+    except Exception as e:
+        print(f"Logging middleware error: {e}")
+
+    print("MIDDLEWARE sees:", getattr(request.state, "cache_status", "NOT_SET"))
+    return response
+
+
+# ── Fast Gemini call ──────────────────────────────────
+def call_gemini_fast(query: str, limit: int = 15) -> list:
+    prompt = f"""List {limit} specific evaluation criteria for: "{query}"
+Rules:
+- Specific to "{query}" only — no competitors
+- 2-5 words each
+- Most important first
+
+Return ONLY a JSON array. Example: ["Battery Life", "On Road Price", "Fuel Efficiency"]
+Generate for: "{query}" """
+
+    for attempt in range(2):
+        try:
+            res = http_requests.post(
+                GEMINI_URL,
+                headers={
+                    "Authorization": f"Bearer {get_access_token()}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                    "generationConfig": {
+                        "temperature": 0.1,
+                        "maxOutputTokens": 250  # small = fast
+                    }
+                },
+                timeout=6
+            )
+            if res.status_code == 429:
+                time.sleep(2 ** attempt)
+                continue
+            res.raise_for_status()
+            raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
+            match = re.search(r'\[.*\]', raw, re.DOTALL)
+            if match:
+                suggestions = json_module.loads(match.group(0))
+                print(f"✅ Fast Gemini: {len(suggestions)} suggestions for '{query}'")
+                return suggestions
+        except http_requests.exceptions.Timeout:
+            print(f"⏰ Gemini timeout attempt {attempt+1}")
+        except Exception as e:
+            print(f"⚠️ Gemini attempt {attempt+1}: {type(e).__name__}")
+        time.sleep(1)
+    return []
+
+
+def call_gemini_more(query: str, existing: list) -> list:
+    """Second background call — 25 more suggestions, different ones"""
+    existing_str = ", ".join(existing[:15])  # send first 15 so Gemini avoids them
+    prompt = f"""List 25 more specific evaluation criteria for: "{query}"
+Rules:
+- Specific to "{query}" only
+- 2-5 words each
+- Do NOT repeat any of these: {existing_str}
+- Different aspects not yet covered
+- Most important first
+
+Return ONLY a JSON array.
+Generate for: "{query}" """
+
+    try:
+        res = http_requests.post(
+            GEMINI_URL,
+            headers={
+                "Authorization": f"Bearer {get_access_token()}",
+                "Content-Type": "application/json"
+            },
+            json={
+                "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                "generationConfig": {
+                    "temperature": 0.3,
+                    "maxOutputTokens": 600
+                }
+            },
+            timeout=15
+        )
+        res.raise_for_status()
+        raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
+        match = re.search(r'\[.*\]', raw, re.DOTALL)
+        if match:
+            more = json_module.loads(match.group(0))
+            # filter out any duplicates
+            existing_lower = [e.lower() for e in existing]
+            return [s for s in more if s.lower() not in existing_lower]
+    except Exception as e:
+        print(f"⚠️ call_gemini_more failed: {type(e).__name__}")
+    return []
+
+# ── Background store in Redis + DB ───────────────────
+def store_in_background(query: str, suggestions: list, r=None, cache_key: str = None):
+    def _store():
+        if r and cache_key:
+            try:
+                ttl = 86400 if len(query.split()) >= 3 else 1800
+                r.setex(cache_key, ttl, json_module.dumps(suggestions))
+                print(f"💾 Redis cached: '{query}' ({len(suggestions)} items)")
+            except Exception:
+                pass
+        try:
+            bootstrap_domain(query)
+            print(f"✅ DB bootstrapped: '{query}'")
+            if r and cache_key:
+                try:
+                    r.delete(cache_key)
+                    print(f"🗑️ Redis cleared → DB now serves '{query}'")
+                except Exception:
+                    pass
+        except Exception as e:
+            print(f"⚠️ Bootstrap failed: {type(e).__name__}")
+
+    threading.Thread(target=_store, daemon=True).start()
+
+
+# ── Main get suggestions function ────────────────────
+def get_suggestions_fast(query: str, limit: int = 15, existing: list = None) -> dict:
+    existing = existing or []
+
+    # ── L1: Redis cache ──────────────────────────────
+    r = get_redis()
+    cache_key = get_live_cache_key(query)
+
+    if r:
+        try:
+            cached = r.get(cache_key)
+            if cached:
+                all_suggestions = json_module.loads(cached)
+                new_only = [s for s in all_suggestions if s not in existing]
+                print(f"⚡ Redis HIT: '{query}' → {len(new_only)} available")
+                return {"suggestions": new_only[:limit], "cache": "redis_hit"}
+        except Exception:
+            pass
+
+    # ── L2: DB (pgvector) ────────────────────────────
+    try:
+        normalized = normalize_query(query.strip())
+        embedding = get_or_encode(normalized, model)
+        emb_param = str(embedding)
+
+        with engine.begin() as conn:
+            domain_row = conn.execute(text("""
+                SELECT id, name,
+                       embedding <-> CAST(:emb AS vector) AS distance
+                FROM domains ORDER BY distance LIMIT 1
+            """), {"emb": emb_param}).fetchone()
+
+            if domain_row and domain_row.distance <= 0.8:
+                results = conn.execute(text("""
+                    SELECT a.name,
+                           (1 - (a.embedding <-> CAST(:emb AS vector))) * 0.7
+                           + LEAST(COALESCE(af.click_count, 0), 100) * 0.003 AS score
+                    FROM attributes a
+                    JOIN dimension_groups g ON a.group_id = g.id
+                    LEFT JOIN attribute_feedback af
+                        ON af.attribute_name = a.name
+                        AND af.domain = :domain_name
+                    WHERE g.domain_id = :domain_id
+                    ORDER BY score DESC
+                    LIMIT :limit
+                """), {"emb": emb_param, "domain_id": domain_row.id,
+                       "domain_name": domain_row.name, "limit": limit * 2})
+
+                db_suggestions = [row[0] for row in results]
+                new_only = [s for s in db_suggestions if s not in existing]
+
+                if new_only:
+                    print(f"⚡ DB HIT: '{query}' → {len(new_only)} suggestions")
+                    if r:
+                        try:
+                            r.setex(cache_key, 3600, json_module.dumps(db_suggestions))
+                        except Exception:
+                            pass
+                    return {"suggestions": new_only[:limit], "cache": "db_hit",
+                            "domain": domain_row.name}
+    except Exception as e:
+        print(f"⚠️ DB check failed: {type(e).__name__}")
+
+    # ── L3: Gemini fast (new query) ──────────────────
+    print(f"🔄 New query — calling Gemini fast: '{query}'")
+    suggestions = call_gemini_fast(query, limit=15)
+
+    if suggestions:
+        # Background: fetch 25 more + store everything in Redis
+        def fetch_more_and_store():
+            more = call_gemini_more(query, suggestions)
+            all_suggestions = suggestions + more
+            print(f"✅ Total suggestions after background fetch: {len(all_suggestions)}")
+            store_in_background(query, all_suggestions, r, cache_key)
+
+        threading.Thread(target=fetch_more_and_store, daemon=True).start()
+
+        new_only = [s for s in suggestions if s not in existing]
+        return {"suggestions": new_only[:limit], "cache": "gemini_fast"}
+
+    return {"suggestions": [], "cache": "gemini_failed"}
+
+
+# ── Category suggestions via Gemini ──────────────────
+def generate_category_suggestions(query: str, category: str, existing: list, limit: int = 15):
+    category_context = {
+        "shopping": "buying options, best price, where to buy, deals, discounts, EMI, cashback, online vs offline, delivery, sellers",
+        "images":   "exterior design, interior photos, color options, visual features, styling, aesthetics, dimensions look, photo gallery",
+        "videos":   "video reviews, test drive videos, owner reviews, comparison videos, YouTube channels, expert opinions, unboxing",
+        "news":     "latest news, recent updates, new model announcement, price change, upcoming launch, recalls, awards won",
+        "places":   "best dealers, showrooms near me, service centers, test drive locations, authorized dealers, city availability"
+    }
+    context = category_context.get(category, "general evaluation")
+    prompt = f"""The user is searching for "{query}" in {category.upper()} category.
+Generate {limit} specific keyword suggestions related to "{query}" for the {category} context.
+Focus on: {context}
+Rules:
+- Each suggestion must be 2-5 words
+- Must be directly related to "{query}" in {category} context
+- Order by most searched/relevant first
+Return ONLY a JSON array of strings. No explanation."""
+
+    for attempt in range(3):
+        try:
+            res = http_requests.post(
+                GEMINI_URL,
+                headers={
+                    "Authorization": f"Bearer {get_access_token()}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                    "generationConfig": {"temperature": 0.4, "maxOutputTokens": 512}
+                },
+                timeout=30
+            )
+            if res.status_code == 429:
+                time.sleep(2 ** attempt)
+                continue
+            res.raise_for_status()
+            raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
+            match = re.search(r'\[.*\]', raw, re.DOTALL)
+            if match:
+                suggestions = json_module.loads(match.group(0))
+                return {"suggestions": suggestions[:limit], "cache": "category_ai"}
+            return {"suggestions": [], "cache": "parse_error"}
+        except http_requests.exceptions.ConnectionError as e:
+            print(f"⚠️ Connection error attempt {attempt + 1}: {type(e).__name__}")
+            if attempt < 2:
+                time.sleep(2)
+                continue
+            return {"suggestions": [], "cache": "connection_error"}
+        except Exception as e:
+            print(f"❌ Category suggestions failed: {type(e).__name__}")
+            return {"suggestions": [], "cache": "error"}
+    return {"suggestions": [], "cache": "failed"}
+
+
+# ── serve_from_db ─────────────────────────────────────
+def serve_from_db(conn, domain_row, emb_param, limit, offset):
+    results = conn.execute(text("""
+        SELECT a.name,
+               (1 - (a.embedding <-> CAST(:emb AS vector))) * 0.7
+               + LEAST(COALESCE(af.click_count, 0), 100) * 0.003 AS score
+        FROM attributes a
+        JOIN dimension_groups g ON a.group_id = g.id
+        LEFT JOIN attribute_feedback af ON af.attribute_name = a.name
+        WHERE g.domain_id = :domain_id
+        ORDER BY score DESC
+        LIMIT :limit OFFSET :offset
+    """), {"emb": emb_param, "domain_id": domain_row.id,
+           "limit": limit, "offset": offset})
+    return [r[0] for r in results]
+
+
 # ── /suggest endpoint ─────────────────────────────────
 @app.get("/suggest")
-def suggest(query: str, offset: int = 0, limit: int = 15):
+def suggest(request: Request, query: str, offset: int = 0, limit: int = 15, category: str = "general"):
+
+    def build_response(data, status):
+        request.state.cache_status = status
+        data["cache"] = status
+        return data
+
+    start = time.time()
+
     if len(query.strip()) < 2:
-        return {"suggestions": [], "cache": "skip"}
+        return build_response({"suggestions": []}, "skip")
 
-    # Block gibberish server-side
     if is_gibberish(query.strip()):
-        return {"suggestions": [], "cache": "gibberish"}
+        return build_response({"suggestions": []}, "gibberish")
 
-    normalized = normalize_query(query.strip())
-    print(f"📝 Normalized: '{query}' → '{normalized}'")
-
-    embedding = get_or_encode(normalized, model)
-    word_count = len(query.strip().split())
+    # Category → AI directly
+    if category != "general":
+        result = generate_category_suggestions(query.strip(), category, [], limit)
+        return build_response(result, "category_ai")
 
-    # Use semantic cache only for full queries (3+ words) on first page
-    if word_count >= 3 and offset == 0:
-        cached = get_semantic_cache(embedding, domain=normalized)
-        if cached:
-            print(f"✅ Semantic cache HIT")
-            return {"suggestions": cached[offset:offset + limit], "cache": "semantic_hit"}
-
-    emb_param = str(embedding)
-    with engine.begin() as conn:
-        domain_row = conn.execute(text("""
-            SELECT id, name,
-                   embedding <-> CAST(:emb AS vector) AS distance
-            FROM domains
-            ORDER BY distance
-            LIMIT 1
-        """), {"emb": emb_param}).fetchone()
-
-        if domain_row is None or domain_row.distance > 0.8:
-            if not is_gibberish(query):
-                try:
-                    bootstrap_domain(query)
-                    print(f"✅ Bootstrapped: {query}")
-                except Exception as e:
-                    print(f"⚠️ Bootstrap failed: {e}")
-
-                domain_row = conn.execute(text("""
-                    SELECT id, name
-                    FROM domains
-                    ORDER BY embedding <-> CAST(:emb AS vector)
-                    LIMIT 1
-                """), {"emb": emb_param}).fetchone()
-
-                if domain_row is None:
-                    return {"suggestions": [], "cache": "no_domain"}
-                # ✅ continues below to fetch attributes
-            else:
-                return {"suggestions": [], "cache": "no_domain"}
-
-        results = conn.execute(text("""
-            SELECT a.name,
-                   1 - (a.embedding <-> CAST(:emb AS vector)) AS score
-            FROM attributes a
-            JOIN dimension_groups g ON a.group_id = g.id
-            WHERE g.domain_id = :domain_id
-            ORDER BY score DESC
-            LIMIT :limit OFFSET :offset
-        """), {"emb": emb_param, "domain_id": domain_row.id,
-               "limit": limit, "offset": offset})
-
-        suggestions = [r[0] for r in results]
-        domain_name = domain_row.name
-
-    # Deduplicate
-    seen = set()
-    ranked = []
-    for name in suggestions:
-        if name.lower() not in seen:
-            seen.add(name.lower())
-            ranked.append(name)
-
-    # Cache only full queries on first page
-    if word_count >= 3 and offset == 0:
-        write_cache_async(normalized, embedding, ranked, domain=domain_name)
-
-    return {"suggestions": ranked, "cache": "miss", "domain": domain_name}
+    normalized = normalize_query(query.strip())
+    print(f"Normalized: '{query}' → '{normalized}'")
+
+    # ── Semantic cache check ──────────────────────────
+    try:
+        embedding = get_or_encode(normalized, model)
+        emb_param = str(embedding)
+
+        with engine.begin() as conn:
+            domain_row = conn.execute(text("""
+                SELECT id, name,
+                       embedding <-> CAST(:emb AS vector) AS distance
+                FROM domains ORDER BY distance LIMIT 1
+            """), {"emb": emb_param}).fetchone()
+
+            if domain_row and domain_row.distance <= 0.8:
+                domain_name = domain_row.name
+
+                if offset == 0:
+                    cached = get_semantic_cache(embedding, domain=domain_name)
+                    if cached:
+                        distance = cached["distance"]
+                        cached_query = cached["query"]
+                        suggestions = cached["suggestions"]
+                        print(f"Cache found → distance: {distance:.4f}, query: {cached_query}")
+
+                        def is_relevant(q, cq):
+                            q_words = set(q.lower().split())
+                            c_words = set(cq.lower().split())
+                            overlap = len(q_words & c_words) / max(len(q_words), 1)
+                            return overlap > 0.5
+
+                        if distance < 0.05 and is_relevant(normalized, cached_query):
+                            print("✅ Strong semantic cache HIT")
+                            return build_response(
+                                {"suggestions": suggestions[:limit], "domain": domain_name},
+                                "strong_hit"
+                            )
+                        elif distance < 0.1 and is_relevant(normalized, cached_query):
+                            print("⚡ Medium match → refining")
+                            fresh_results = conn.execute(text("""
+                                SELECT a.name,
+                                       1 - (a.embedding <-> CAST(:emb AS vector)) AS score
+                                FROM attributes a
+                                JOIN dimension_groups g ON a.group_id = g.id
+                                WHERE g.domain_id = :domain_id
+                                ORDER BY score DESC
+                                LIMIT :limit
+                            """), {"emb": emb_param, "domain_id": domain_row.id, "limit": limit})
+                            fresh = [r[0] for r in fresh_results]
+                            merged = []
+                            seen = set()
+                            for item in suggestions + fresh:
+                                if item.lower() not in seen:
+                                    seen.add(item.lower())
+                                    merged.append(item)
+                            return build_response(
+                                {"suggestions": merged[:limit], "domain": domain_name},
+                                "refined_hit"
+                            )
+                        else:
+                            print("❌ Cache ignored (low relevance)")
+
+                results = conn.execute(text("""
+                    SELECT a.name,
+                           (1 - (a.embedding <-> CAST(:emb AS vector))) * 0.7
+                           + LEAST(COALESCE(af.click_count, 0), 100) * 0.003 AS score
+                    FROM attributes a
+                    JOIN dimension_groups g ON a.group_id = g.id
+                    LEFT JOIN attribute_feedback af
+                        ON af.attribute_name = a.name
+                        AND af.domain = :domain_name
+                    WHERE g.domain_id = :domain_id
+                    ORDER BY score DESC
+                    LIMIT :limit OFFSET :offset
+                """), {"emb": emb_param, "domain_id": domain_row.id,
+                       "domain_name": domain_name, "limit": limit, "offset": offset})
+
+                suggestions = [r[0] for r in results]
+                seen = set()
+                ranked = []
+                for name in suggestions:
+                    if name.lower() not in seen:
+                        seen.add(name.lower())
+                        ranked.append(name)
+
+                if ranked:
+                    if offset == 0 and len(normalized.split()) >= 2:
+                        write_cache_async(normalized, embedding, ranked, domain=domain_name)
+                    print(f"⚡ DB HIT: '{query}' → {len(ranked)} suggestions in {int((time.time()-start)*1000)}ms")
+                    return build_response(
+                        {"suggestions": ranked, "domain": domain_name},
+                        "db_hit"
+                    )
+
+    except Exception as e:
+        print(f"⚠️ DB/cache check failed: {type(e).__name__}")
+
+    # ── Fast Gemini fallback ──────────────────────────
+    print(f"🔄 Fast Gemini for: '{query}'")
+    result = get_suggestions_fast(query, limit=40)
+    return build_response(result, result.get("cache", "gemini_fast"))
+
+
+# ── /suggest/more endpoint ────────────────────────────
+@app.get("/suggest/more")
+def suggest_more(query: str, category: str = "general", existing: str = ""):
+    existing_list = [e.strip() for e in existing.split(",") if e.strip()]
+
+    if category == "general":
+        r = get_redis()
+        if r:
+            try:
+                cache_key = get_live_cache_key(query)
+                cached = r.get(cache_key)
+                if cached:
+                    all_suggestions = json_module.loads(cached)
+                    new_only = [s for s in all_suggestions if s not in existing_list]
+                    print(f"⚡ Instant more: {len(new_only)} from cache")
+                    return {"suggestions": new_only[:12], "cache": "live_hit"}
+            except Exception:
+                pass
+
+        result = get_suggestions_fast(query, limit=12, existing=existing_list)
+        return result
+
+    category_context = {
+        "shopping": "focus on buying, pricing, deals, offers, sellers, delivery, payment options",
+        "images":   "focus on visual aspects, design, appearance, colors, aesthetics, photos",
+        "videos":   "focus on reviews, test drives, comparisons, tutorials, unboxing, demos",
+        "news":     "focus on latest updates, recent changes, announcements, trends, events",
+        "places":   "focus on locations, dealers, service centers, showrooms, nearby options",
+    }
+    context = category_context.get(category, "general evaluation")
+    prompt = f"""Generate 12 more keyword suggestions for "{query}" in {category.upper()} context.
+Focus on: {context}
+Do NOT repeat: {', '.join(existing_list) if existing_list else 'none'}
+Each suggestion 2-5 words max.
+Return ONLY a JSON array."""
+
+    try:
+        res = http_requests.post(
+            GEMINI_URL,
+            headers={"Authorization": f"Bearer {get_access_token()}", "Content-Type": "application/json"},
+            json={"contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                  "generationConfig": {"temperature": 0.5, "maxOutputTokens": 300}},
+            timeout=15)
+        res.raise_for_status()
+        raw = res.json()["candidates"][0]["content"]["parts"][0]["text"]
+        match = re.search(r'\[.*\]', raw, re.DOTALL)
+        if match:
+            suggestions = json_module.loads(match.group(0))
+            return {"suggestions": [s for s in suggestions if s not in existing_list][:12],
+                    "cache": "ai_generated"}
+        return {"suggestions": [], "cache": "parse_error"}
+    except Exception as e:
+        print(f"❌ suggest/more failed: {type(e).__name__}")
+        return {"suggestions": [], "cache": "error"}
+
+
+# ── /search/videos endpoint ───────────────────────────
+@app.get("/search/videos")
+def search_videos(query: str):
+    if not GOOGLE_SEARCH_API_KEY:
+        return {"results": [], "error": "API key not configured"}
+    try:
+        url = "https://www.googleapis.com/youtube/v3/search"
+        params = {
+            "part": "snippet",
+            "q": query,
+            "type": "video",
+            "maxResults": 6,
+            "order": "relevance",
+            "key": GOOGLE_SEARCH_API_KEY
+        }
+        res = http_requests.get(url, params=params, timeout=10)
+        res.raise_for_status()
+        data = res.json()
+        results = []
+        for item in data.get("items", []):
+            snippet = item["snippet"]
+            video_id = item["id"]["videoId"]
+            results.append({
+                "title": snippet["title"],
+                "channel": snippet["channelTitle"],
+                "thumbnail": snippet["thumbnails"]["medium"]["url"],
+                "url": f"https://www.youtube.com/watch?v={video_id}",
+                "published": snippet["publishedAt"][:10],
+                "description": snippet.get("description", "")[:120]
+            })
+        return {"results": results, "query": query}
+    except Exception as e:
+        print(f"❌ YouTube search failed: {type(e).__name__}")
+        return {"results": [], "error": "Search unavailable. Please try again."}
+
+
+# ── /search/shopping endpoint ─────────────────────────
+@app.get("/search/shopping")
+def search_shopping(query: str):
+    if not GOOGLE_SEARCH_API_KEY or not GOOGLE_SEARCH_CX:
+        return {"results": [], "error": "Google Search API not configured"}
+    try:
+        url = "https://www.googleapis.com/customsearch/v1"
+        params = {
+            "q": f"{query} buy price",
+            "cx": GOOGLE_SEARCH_CX,
+            "key": GOOGLE_SEARCH_API_KEY,
+            "num": 6
+        }
+        res = http_requests.get(url, params=params, timeout=10)
+        res.raise_for_status()
+        data = res.json()
+        results = []
+        for item in data.get("items", []):
+            results.append({
+                "title": item.get("title", ""),
+                "link": item.get("link", ""),
+                "snippet": item.get("snippet", "")[:150],
+                "source": item.get("displayLink", ""),
+                "image": item.get("pagemap", {}).get("cse_image", [{}])[0].get("src", "")
+            })
+        return {"results": results, "query": query}
+    except Exception as e:
+        print(f"❌ Shopping search failed: {type(e).__name__}")
+        return {"results": [], "error": "Search unavailable. Please try again."}
+
+
+# ── /search/images endpoint ───────────────────────────
+@app.get("/search/images")
+def search_images(query: str):
+    if not GOOGLE_SEARCH_API_KEY or not GOOGLE_SEARCH_CX:
+        return {"results": [], "error": "Google Search API not configured"}
+    try:
+        url = "https://www.googleapis.com/customsearch/v1"
+        params = {
+            "q": query,
+            "cx": GOOGLE_SEARCH_CX,
+            "key": GOOGLE_SEARCH_API_KEY,
+            "searchType": "image",
+            "num": 6,
+            "safe": "active"
+        }
+        res = http_requests.get(url, params=params, timeout=10)
+        res.raise_for_status()
+        data = res.json()
+        results = []
+        for item in data.get("items", []):
+            results.append({
+                "title": item.get("title", ""),
+                "link": item.get("link", ""),
+                "source": item.get("displayLink", ""),
+                "thumbnail": item.get("image", {}).get("thumbnailLink", ""),
+                "context_link": item.get("image", {}).get("contextLink", "")
+            })
+        return {"results": results, "query": query}
+    except Exception as e:
+        print(f"❌ Image search failed: {type(e).__name__}")
+        return {"results": [], "error": "Search unavailable. Please try again."}
+
+
+# ── /search/news endpoint ─────────────────────────────
+@app.get("/search/news")
+def search_news(query: str):
+    if not GOOGLE_SEARCH_API_KEY or not GOOGLE_SEARCH_CX:
+        return {"results": [], "error": "Google Search API not configured"}
+    try:
+        url = "https://www.googleapis.com/customsearch/v1"
+        params = {
+            "q": f"{query} news 2025",
+            "cx": GOOGLE_SEARCH_CX,
+            "key": GOOGLE_SEARCH_API_KEY,
+            "num": 6,
+            "sort": "date"
+        }
+        res = http_requests.get(url, params=params, timeout=10)
+        res.raise_for_status()
+        data = res.json()
+        results = []
+        for item in data.get("items", []):
+            results.append({
+                "title": item.get("title", ""),
+                "link": item.get("link", ""),
+                "snippet": item.get("snippet", "")[:200],
+                "source": item.get("displayLink", ""),
+                "image": item.get("pagemap", {}).get("cse_image", [{}])[0].get("src", "")
+            })
+        return {"results": results, "query": query}
+    except Exception as e:
+        print(f"❌ News search failed: {type(e).__name__}")
+        return {"results": [], "error": "Search unavailable. Please try again."}
+
+
+# ── /search/places endpoint ───────────────────────────
+@app.get("/search/places")
+def search_places(query: str):
+    if not GOOGLE_SEARCH_API_KEY or not GOOGLE_SEARCH_CX:
+        return {"results": [], "error": "Google Search API not configured"}
+    try:
+        url = "https://www.googleapis.com/customsearch/v1"
+        params = {
+            "q": f"{query} near me dealers showroom location",
+            "cx": GOOGLE_SEARCH_CX,
+            "key": GOOGLE_SEARCH_API_KEY,
+            "num": 6
+        }
+        res = http_requests.get(url, params=params, timeout=10)
+        res.raise_for_status()
+        data = res.json()
+        results = []
+        for item in data.get("items", []):
+            results.append({
+                "title": item.get("title", ""),
+                "link": item.get("link", ""),
+                "snippet": item.get("snippet", "")[:200],
+                "source": item.get("displayLink", ""),
+                "image": item.get("pagemap", {}).get("cse_image", [{}])[0].get("src", "")
+            })
+        return {"results": results, "query": query}
+    except Exception as e:
+        print(f"❌ Places search failed: {type(e).__name__}")
+        return {"results": [], "error": "Search unavailable. Please try again."}
 
 
 # ── /generate endpoint ────────────────────────────────
 class GenerateRequest(BaseModel):
     query: str
     selected_attributes: list[str]
+    category: str = "general"
     chat_history: list[dict] = []
 
 
@@ -181,46 +762,48 @@ def generate(request: GenerateRequest):
 
     attributes = ", ".join(request.selected_attributes) if request.selected_attributes else "general evaluation"
 
-    prompt = f"""{history_text}USER QUESTION: "{request.query}"
-EVALUATION CRITERIA SELECTED: {attributes}
-
-You are an expert advisor. The user has specifically asked about "{request.query}".
-Your job is to answer the user's question "{request.query}" and analyze it through each of the selected criteria.
+    category_focus = {
+        "shopping": "Focus on buying options, best prices, deals, where to buy, payment plans, EMI options.",
+        "images":   "Focus on visual design, appearance, color options, exterior/interior aesthetics.",
+        "videos":   "Focus on video reviews, test drives, comparisons, what reviewers say.",
+        "news":     "Focus on latest news, recent updates, upcoming changes, current trends.",
+        "places":   "Focus on best places to buy, dealers, showrooms, service centers.",
+        "general":  ""
+    }.get(request.category, "")
 
-Answer the question "{request.query}" directly first in 2-3 sentences.
-Then for each criterion in [{attributes}], explain how it applies specifically to "{request.query}".
+    prompt = f"""{history_text}USER QUESTION: "{request.query}"
+CATEGORY: {request.category.upper()}
+{category_focus}
+EVALUATION CRITERIA: {attributes}
 
-Format your response as:
+You are an expert advisor. Answer "{request.query}" directly and analyze through each selected criterion.
 
+Format:
 ## About: {request.query}
-[Direct answer to the user's question in 2-3 sentences]
+[Direct answer in 2-3 sentences]
 
 ---
-[For each selected criterion:]
 **[Criterion Name]**
-- How this applies to "{request.query}"
-- Specific facts, numbers, or data
+- How it applies to "{request.query}"
+- Specific facts or numbers
 - Recommendation
 
 ---
 ## Bottom Line
-[2-3 sentence summary answering: should the user go with "{request.query}"? What should they prioritize?]
+[2-3 sentence summary]
 
-STRICT RULES:
+RULES:
 - Every sentence must be about "{request.query}" specifically
-- Never give generic advice not related to "{request.query}"
-- If unsure about a fact, say "verify on official website"
 - Use real numbers where confident
-- Total response under 400 words"""
-
-    url = f"https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{GOOGLE_PROJECT_ID}/locations/{LOCATION}/publishers/google/models/gemini-2.5-flash-lite:generateContent"
+- If unsure say "verify on official website"
+- Total under 400 words"""
 
     print(f"🔄 Calling Vertex AI for: {request.query}")
 
     for attempt in range(3):
         try:
             res = http_requests.post(
-                url,
+                GEMINI_URL,
                 headers={
                     "Authorization": f"Bearer {get_access_token()}",
                     "Content-Type": "application/json"
@@ -231,25 +814,47 @@ STRICT RULES:
                 },
                 timeout=30
             )
-
             if res.status_code == 429:
                 wait = 2 ** attempt
                 print(f"⏳ Rate limited, waiting {wait}s...")
                 time.sleep(wait)
                 continue
-
             print(f"✅ Vertex response: {res.status_code}")
             res.raise_for_status()
             answer = res.json()["candidates"][0]["content"]["parts"][0]["text"]
             return {"answer": answer}
-
         except http_requests.exceptions.Timeout:
             print(f"⏰ Timeout on attempt {attempt + 1}")
             if attempt == 2:
                 return {"answer": "Request timed out. Please try again."}
             time.sleep(2)
         except Exception as e:
-            print(f"❌ Error: {e}")
+            print(f"❌ Error: {type(e).__name__}")
             return {"answer": "Error getting response. Please try again."}
 
     return {"answer": "Could not get response after 3 attempts. Please try again."}
+
+
+# ── /feedback endpoint ────────────────────────────────
+class FeedbackRequest(BaseModel):
+    query: str
+    selected_chips: list[str]
+    domain: str = ""
+
+
+@app.post("/feedback")
+def feedback(request: FeedbackRequest):
+    try:
+        with engine.begin() as conn:
+            for chip in request.selected_chips:
+                conn.execute(text("""
+                    INSERT INTO attribute_feedback (query, attribute_name, domain, click_count)
+                    VALUES (:query, :attr, :domain, 1)
+                    ON CONFLICT (query, attribute_name)
+                    DO UPDATE SET click_count = attribute_feedback.click_count + 1,
+                    last_clicked = NOW()
+                """), {"query": request.query, "attr": chip, "domain": request.domain})
+        return {"status": "ok"}
+    except Exception as e:
+        print(f"Feedback write failed: {type(e).__name__}")
+        return {"status": "error"}

app/semantic_cache.py

@@ -81,7 +81,10 @@ def get_semantic_cache(embedding: list, domain: str = None):
 
     if distance < SIMILARITY_THRESHOLD:
         print(f"Semantic cache HIT (distance: {distance:.4f}, domain: {domain})")
-        return json.loads(top.suggestions)
+        return {
+            "query": top.query.decode() if isinstance(top.query, bytes) else top.query,
+            "distance": distance,
+            "suggestions": json.loads(top.suggestions)}
 
     print(f"Semantic cache MISS (distance: {distance:.4f})")
     return None

app/service-account.json

@@ -1,13 +0,0 @@
-{
-  "type": "service_account",
-  "project_id": "sylvan-deck-387207",
-  "private_key_id": "dfcde7ef895c5677b3db9af908ff253155a03c21",
-  "private_key": "-----BEGIN PRIVATE KEY-----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\nQTaufmfSE/AiQcDSc9PbnKPL\n-----END PRIVATE KEY-----\n",
-  "client_email": "sylvan-deck-387207@appspot.gserviceaccount.com",
-  "client_id": "113050669736679470974",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/sylvan-deck-387207%40appspot.gserviceaccount.com",
-  "universe_domain": "googleapis.com"
-}

app/vertex_client.py

@@ -1,12 +1,21 @@
 # app/vertex_client.py
 from google.oauth2 import service_account
 import google.auth.transport.requests
-
-SERVICE_ACCOUNT_FILE = r"C:\Users\rithv\OneDrive\Desktop\decision_engine_project\app\service-account.json"
+import os
 
 def get_access_token():
+    # Try environment variable first
+    creds_path = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
+    
+    # Fallback to absolute path
+    if not creds_path or not os.path.exists(creds_path):
+        creds_path = r"C:\Users\rithv\OneDrive\Desktop\decision_engine_project\app\service-account.json"
+    
+    if not os.path.exists(creds_path):
+        raise FileNotFoundError(f"Service account file not found at: {creds_path}")
+    
     credentials = service_account.Credentials.from_service_account_file(
-        SERVICE_ACCOUNT_FILE,
+        creds_path,
         scopes=["https://www.googleapis.com/auth/cloud-platform"]
     )
     credentials.refresh(google.auth.transport.requests.Request())

docker-compose.yml

@@ -0,0 +1,46 @@
+version: "3.9"
+
+services:
+
+  postgres:
+    image: ankane/pgvector        # ← matches your existing container
+    container_name: decision_pg
+    restart: always
+    environment:
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+      POSTGRES_DB: decision_engine
+    ports:
+      - "5432:5432"
+    volumes:
+      - pg_data:/var/lib/postgresql/data
+
+  redis:
+    image: redis/redis-stack-server:latest
+    container_name: decision_redis
+    restart: always
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis_data:/data
+
+  backend:
+    build: .
+    container_name: decision_backend
+    restart: always
+    ports:
+      - "8000:8000"
+    environment:
+      DATABASE_URL: postgresql://postgres:postgres@postgres:5432/decision_engine
+      GOOGLE_PROJECT_ID: sylvan-deck-387207
+      GOOGLE_APPLICATION_CREDENTIALS: /app/app/service-account.json
+      REDIS_URL: redis://redis:6379
+    volumes:
+      - ./app/service-account.json:/app/app/service-account.json:ro
+    depends_on:
+      - postgres
+      - redis
+
+volumes:
+  pg_data:
+  redis_data:

index.html

@@ -2,84 +2,200 @@
 <html>
 <head>
 <meta charset="UTF-8">
-<title>AI Prompt Compressor Engine</title>
+<title>AI Prompt Composer Engine</title>
 <style>
 *{box-sizing:border-box;margin:0;padding:0}
 body{font-family:Arial;background:#f5f5f5;padding:40px}
-.container{max-width:900px;margin:auto}
+.container{max-width:960px;margin:auto}
 h1{margin-bottom:20px}
 
 .prompt-row{display:flex;gap:8px;width:100%}
 .prompt-row input{flex:1;padding:10px;border:1px solid #ccc;border-radius:8px;font-size:14px}
 
-.extra-row{margin-top:10px}
-.extra-row input{width:100%;padding:10px;border:1px solid #ccc;border-radius:8px;font-size:13px}
+.extra-row{margin-top:10px;display:flex;gap:8px}
+.extra-row input{flex:1;padding:10px;border:1px solid #ccc;border-radius:8px;font-size:13px}
 
 button{padding:10px 16px;border:none;border-radius:8px;background:black;color:white;cursor:pointer;font-size:13px}
 button.secondary{background:#555}
 button:disabled{background:#999;cursor:not-allowed}
 
-.keyword-box{margin-top:20px;padding:16px;background:white;border-radius:10px;border:1px solid #ddd;min-height:80px;width:100%}
-.keyword-box b{display:block;margin-bottom:10px;font-size:14px}
+.clear-field-btn{padding:4px 10px;border:1px solid #ddd;border-radius:6px;background:white;color:#999;cursor:pointer;font-size:12px;transition:all .15s;flex-shrink:0}
+.clear-field-btn:hover{border-color:#ef4444;color:#ef4444;background:#fff5f5}
 
 .selected-area{margin-top:16px;padding:12px 16px;background:white;border-radius:10px;border:1px solid #ddd;display:none}
-.selected-area b{display:block;margin-bottom:8px;font-size:14px}
 
+/* ── Category Tabs ── */
+.category-tabs{display:flex;gap:8px;margin-top:20px;flex-wrap:wrap}
+.cat-tab{display:flex;align-items:center;gap:6px;padding:7px 14px;border-radius:20px;border:1px solid #ddd;background:white;cursor:pointer;font-size:13px;color:#555;transition:all .15s;user-select:none}
+.cat-tab:hover{border-color:#333;color:#111}
+.cat-tab.active{background:#111;color:white;border-color:#111}
+
+/* ── Keyword box ── */
+.keyword-box{margin-top:12px;padding:16px;background:white;border-radius:10px;border:1px solid #ddd;min-height:80px}
+.box-header{display:flex;align-items:center;justify-content:space-between;margin-bottom:10px}
+.box-title{font-size:14px;font-weight:bold}
+
+.category-badge{font-size:11px;padding:3px 10px;border-radius:20px;background:#f0f0f0;color:#555}
+.category-badge.images{background:#fef3c7;color:#d97706}
+.category-badge.shopping{background:#d1fae5;color:#065f46}
+.category-badge.videos{background:#fee2e2;color:#991b1b}
+.category-badge.news{background:#dbeafe;color:#1e40af}
+.category-badge.places{background:#ede9fe;color:#5b21b6}
+.category-badge.general{background:#f0f0f0;color:#555}
+
+/* ── Chips ── */
 .chip{display:inline-block;padding:7px 14px;margin:4px;border-radius:20px;border:1px solid #ccc;cursor:pointer;background:#fafafa;font-size:13px;transition:all .15s}
 .chip:hover{border-color:#000;background:#f0f0f0}
 .chip.selected{background:#16a34a;color:white;border-color:#16a34a}
 .chip.top{border-color:#f59e0b;background:#fffbeb;font-weight:600}
-.chip.top:hover{background:#fef3c7}
 .chip.top.selected{background:#16a34a;border-color:#16a34a;color:white}
 
 .kw-loading-spinner{display:flex;align-items:center;gap:8px;color:#999;font-size:13px;padding:8px 0}
 .spinner{width:16px;height:16px;border:2px solid #ddd;border-top:2px solid #333;border-radius:50%;animation:spin .8s linear infinite;flex-shrink:0}
 @keyframes spin{to{transform:rotate(360deg)}}
-
 .kw-msg{color:#999;font-size:13px;padding:8px 0;display:block}
 
 .load-more-btn{display:inline-block;padding:6px 14px;margin:6px 4px;border-radius:20px;border:1px dashed #ccc;background:white;color:#666;font-size:12px;cursor:pointer;transition:all .2s}
 .load-more-btn:hover{border-color:#333;color:#333;border-style:solid}
 .load-more-btn:disabled{color:#bbb;cursor:not-allowed}
 
+/* ── Results box ── */
+.results-box{margin-top:16px;display:none}
+.results-box.visible{display:block}
+.results-header{display:flex;align-items:center;justify-content:space-between;margin-bottom:12px}
+.results-title{font-size:14px;font-weight:bold;color:#333}
+.results-grid{display:grid;gap:12px;margin-top:8px}
+
+/* Video card */
+.video-card{background:white;border:1px solid #ddd;border-radius:10px;overflow:hidden;cursor:pointer;transition:transform .15s,box-shadow .15s;text-decoration:none;color:inherit;display:block}
+.video-card:hover{transform:translateY(-2px);box-shadow:0 4px 16px rgba(0,0,0,.1)}
+.video-card img{width:100%;height:150px;object-fit:cover}
+.video-card .vc-info{padding:10px}
+.video-card .vc-title{font-size:13px;font-weight:600;margin-bottom:4px;line-height:1.4}
+.video-card .vc-channel{font-size:11px;color:#888}
+.video-card .vc-date{font-size:10px;color:#aaa;margin-top:2px}
+
+/* Shopping card */
+.shop-card{background:white;border:1px solid #ddd;border-radius:10px;padding:14px;cursor:pointer;transition:transform .15s,box-shadow .15s;text-decoration:none;color:inherit;display:block}
+.shop-card:hover{transform:translateY(-2px);box-shadow:0 4px 16px rgba(0,0,0,.1)}
+.shop-card img{width:100%;height:120px;object-fit:contain;margin-bottom:8px;border-radius:6px;background:#f5f5f5}
+.shop-card .sc-title{font-size:13px;font-weight:600;margin-bottom:4px;line-height:1.4}
+.shop-card .sc-source{font-size:11px;color:#16a34a;font-weight:500}
+.shop-card .sc-snippet{font-size:11px;color:#666;margin-top:4px;line-height:1.4}
+
+/* Image card */
+.image-card{background:white;border:1px solid #ddd;border-radius:10px;overflow:hidden;cursor:pointer;transition:transform .15s;text-decoration:none;display:block}
+.image-card:hover{transform:scale(1.02)}
+.image-card img{width:100%;height:160px;object-fit:cover}
+.image-card .ic-title{font-size:11px;color:#666;padding:6px 10px}
+
+/* News card */
+.news-card{background:white;border:1px solid #ddd;border-radius:10px;padding:14px;display:flex;gap:12px;text-decoration:none;color:inherit;transition:box-shadow .15s}
+.news-card:hover{box-shadow:0 4px 16px rgba(0,0,0,.1)}
+.news-card img{width:80px;height:80px;object-fit:cover;border-radius:6px;flex-shrink:0}
+.news-card .nc-info{flex:1}
+.news-card .nc-title{font-size:13px;font-weight:600;line-height:1.4;margin-bottom:4px}
+.news-card .nc-source{font-size:11px;color:#1e40af;font-weight:500}
+.news-card .nc-snippet{font-size:11px;color:#666;margin-top:4px;line-height:1.4}
+
+/* Places card */
+.place-card{background:white;border:1px solid #ddd;border-radius:10px;padding:14px;display:flex;gap:12px;text-decoration:none;color:inherit;transition:box-shadow .15s}
+.place-card:hover{box-shadow:0 4px 16px rgba(0,0,0,.1)}
+.place-card img{width:70px;height:70px;object-fit:cover;border-radius:6px;flex-shrink:0;background:#f5f5f5}
+.place-card .pc-info{flex:1}
+.place-card .pc-title{font-size:13px;font-weight:600;margin-bottom:4px}
+.place-card .pc-source{font-size:11px;color:#5b21b6;font-weight:500}
+.place-card .pc-snippet{font-size:11px;color:#666;margin-top:4px;line-height:1.4}
+
+/* ── Actions ── */
 .actions{margin-top:16px;display:flex;gap:8px;align-items:center}
 
+/* ── Chat ── */
 .chat-block{margin-top:24px;background:white;padding:20px;border-radius:10px;border:1px solid #ddd}
-.chat-block .go-label{font-weight:bold;font-size:15px;margin-bottom:10px}
-.chat-block .prompt-line{margin:4px 0;font-size:14px;color:#333}
-.chat-block .kw-line{margin:4px 0;font-size:13px;color:#555}
+.go-label{font-weight:bold;font-size:15px;margin-bottom:10px}
+.prompt-line{margin:4px 0;font-size:14px;color:#333}
+.kw-line{margin:4px 0;font-size:13px;color:#555}
+.cat-line{margin:4px 0;font-size:12px;color:#888}
 .answer{background:#f1f1f1;padding:14px;border-radius:8px;margin-top:12px;line-height:1.7;font-size:14px}
 .answer strong{color:#111}
 .answer h2{font-size:15px;margin:10px 0 6px}
+
+/* ── Skeleton shimmer ── */
+.chip.skeleton{
+  background:linear-gradient(90deg,#f0f0f0 25%,#e0e0e0 50%,#f0f0f0 75%);
+  background-size:200% 100%;
+  animation:shimmer 1.2s infinite;
+  border-color:transparent;
+  cursor:default;
+  min-width:60px;
+  height:34px;
+}
+@keyframes shimmer{
+  0%{background-position:200% 0}
+  100%{background-position:-200% 0}
+}
 </style>
 </head>
 <body>
-
 <div class="container">
-  <h1>AI Prompt Compressor Engine</h1>
+  <h1>AI Prompt Composer Engine</h1>
 
+  <!-- Prompt -->
   <div class="prompt-row">
     <input type="text" id="prompt" placeholder="Type your prompt or topic..." />
+    <button type="button" class="clear-field-btn" onclick="clearPrompt()">✕</button>
     <button type="button" id="goBtn" onclick="askAI()">GO</button>
   </div>
 
+  <!-- Extra -->
   <div class="extra-row">
-    <input type="text" id="extra" placeholder="Add extra instruction to refine the prompt (optional)" />
+    <input type="text" id="extra" placeholder="Add extra instruction (optional)" />
+    <button type="button" class="clear-field-btn" onclick="clearExtra()">✕</button>
   </div>
 
+  <!-- Selected keywords -->
   <div class="selected-area" id="selectedArea">
-    <b>Selected Keywords</b>
+    <div style="display:flex;justify-content:space-between;align-items:center;margin-bottom:8px">
+      <b>Selected Keywords</b>
+      <button type="button" class="clear-field-btn" onclick="clearSelected()">✕ Clear</button>
+    </div>
     <div id="selectedKeywords"></div>
   </div>
 
+  <!-- Category Tabs -->
+  <div class="category-tabs">
+    <div class="cat-tab active" data-cat="general"  onclick="selectCategory('general')">🔍 General</div>
+    <div class="cat-tab" data-cat="shopping" onclick="selectCategory('shopping')">🛍️ Shopping</div>
+    <div class="cat-tab" data-cat="images"   onclick="selectCategory('images')">🖼️ Images</div>
+    <div class="cat-tab" data-cat="videos"   onclick="selectCategory('videos')">🎬 Videos</div>
+    <div class="cat-tab" data-cat="news"     onclick="selectCategory('news')">📰 News</div>
+    <div class="cat-tab" data-cat="places"   onclick="selectCategory('places')">📍 Places</div>
+  </div>
+
+  <!-- Suggestions -->
   <div class="keyword-box">
-    <b>Suggestions</b>
+    <div class="box-header">
+      <span class="box-title">Suggestions</span>
+      <div style="display:flex;align-items:center;gap:8px">
+        <span class="category-badge general" id="categoryBadge">General</span>
+        <button type="button" class="clear-field-btn" onclick="clearSuggestions()">✕</button>
+      </div>
+    </div>
     <div id="keywords"><span class="kw-msg">Start typing to see suggestions...</span></div>
   </div>
 
+  <!-- Results box -->
+  <div class="results-box" id="resultsBox">
+    <div class="results-header">
+      <span class="results-title" id="resultsTitle"></span>
+    </div>
+    <div class="results-grid" id="resultsGrid"></div>
+  </div>
+
+  <!-- Actions -->
   <div class="actions">
     <button type="button" id="askBtn" onclick="askAI()">Ask AI</button>
-    <button type="button" class="secondary" onclick="clearAll()">Clear</button>
+    <button type="button" class="secondary" onclick="clearAll()">Clear All</button>
   </div>
 
   <div id="chat"></div>
@@ -92,114 +208,239 @@ button:disabled{background:#999;cursor:not-allowed}
   let debounceTimer;
   let currentQuery = "";
   let currentOffset = 0;
-  let lastFetchedQuery = ""; // tracks last fetched query to avoid duplicate calls
+  let lastFetchedQuery = "";
+  let currentCategory = "general";
 
   const promptInput  = document.getElementById("prompt");
   const keywordsDiv  = document.getElementById("keywords");
   const selectedDiv  = document.getElementById("selectedKeywords");
   const selectedArea = document.getElementById("selectedArea");
 
-  // ── Gibberish detector ──
-  function isGibberish(text) {
-  const words = text.toLowerCase().split(/\s+/);
-  let gibberishCount = 0;
-
-  for (const word of words) {
-    if (word.length <= 3) continue; // allow short words like ev, ai, bmw
+  const CATEGORIES = {
+    general:  { label: "General",  badge: "general",  icon: "🔍" },
+    shopping: { label: "Shopping", badge: "shopping", icon: "🛍️" },
+    images:   { label: "Images",   badge: "images",   icon: "🖼️" },
+    videos:   { label: "Videos",   badge: "videos",   icon: "🎬" },
+    news:     { label: "News",     badge: "news",     icon: "📰" },
+    places:   { label: "Places",   badge: "places",   icon: "📍" }
+  };
 
-    if (word.match(/[^aeiou]{6,}/)) { gibberishCount++; continue; } // raised to 6
+  // ── Gibberish check ──
+  function isGibberish(text) {
+    const words = text.toLowerCase().split(/\s+/);
+    let gc = 0;
+    for (const w of words) {
+      if (w.length <= 3) continue;
+      if (w.match(/[^aeiou]{6,}/)) { gc++; continue; }
+      const v = (w.match(/[aeiou]/g) || []).length;
+      if (w.length > 5 && v / w.length < 0.1) { gc++; continue; }
+      if (/[a-z]\d{3,}[a-z]|[0-9]{4,}[a-z]/.test(w)) { gc++; continue; }
+    }
+    return gc > words.length / 2;
+  }
 
-    const vowels = (word.match(/[aeiou]/g) || []).length;
-    if (word.length > 5 && vowels / word.length < 0.1) { gibberishCount++; continue; }
+  // ── Select category ──
+  function selectCategory(cat) {
+    currentCategory = cat;
+    document.querySelectorAll(".cat-tab").forEach(t => t.classList.remove("active"));
+    document.querySelector(`[data-cat="${cat}"]`).classList.add("active");
+    const badge = document.getElementById("categoryBadge");
+    badge.textContent = CATEGORIES[cat].label;
+    badge.className = `category-badge ${cat}`;
+    lastFetchedQuery = "";
 
-    if (/[a-z]\d{3,}[a-z]|[0-9]{4,}[a-z]/.test(word)) { gibberishCount++; continue; }
+    const q = promptInput.value.trim();
+    if (q.length >= 3 && !isGibberish(q)) {
+      keywordsDiv.innerHTML = `<div class="kw-loading-spinner"><div class="spinner"></div><span>Loading ${CATEGORIES[cat].label} suggestions...</span></div>`;
+      clearTimeout(debounceTimer);
+      debounceTimer = setTimeout(() => fetchKeywords(q), 300);
+      if (cat !== "general") fetchCategoryResults(q, cat);
+      else document.getElementById("resultsBox").classList.remove("visible");
+    } else {
+      document.getElementById("resultsBox").classList.remove("visible");
+    }
   }
 
-  return gibberishCount > words.length / 2;
-}
-
-  // ── Listen to typing ──
+  // ── Typing listener ──
   promptInput.addEventListener("input", () => {
     clearTimeout(debounceTimer);
     const q = promptInput.value.trim();
     currentQuery = q;
 
-    if (q.length < 3) {
+    if (q.length < 4) {
       keywordsDiv.innerHTML = '<span class="kw-msg">Keep typing...</span>';
+      document.getElementById("resultsBox").classList.remove("visible");
       return;
     }
-
     if (isGibberish(q)) {
       keywordsDiv.innerHTML = '<span class="kw-msg">⚠️ Please enter a meaningful topic.</span>';
       return;
     }
 
-    keywordsDiv.innerHTML = `
-      <div class="kw-loading-spinner">
-        <div class="spinner"></div>
-        <span>Loading suggestions for "<b>${q}</b>"...</span>
-      </div>
-    `;
+    if (!keywordsDiv.querySelector('.chip')) {
+      showSkeletonChips();
+    }
 
-    debounceTimer = setTimeout(() => fetchKeywords(q), 500);
+    debounceTimer = setTimeout(() => {
+      fetchKeywords(q);
+      if (currentCategory !== "general") fetchCategoryResults(q, currentCategory);
+    }, 1000);
   });
 
+  // ── Skeleton chips ──
+  function showSkeletonChips() {
+    keywordsDiv.innerHTML = "";
+    for (let i = 0; i < 8; i++) {
+      const sk = document.createElement("span");
+      sk.className = "chip skeleton";
+      sk.style.width = `${60 + Math.random() * 60}px`;
+      sk.innerHTML = "&nbsp;";
+      keywordsDiv.appendChild(sk);
+    }
+  }
+
   // ── Fetch suggestions ──
   async function fetchKeywords(query) {
-    // Skip if same query already fetched — avoids duplicate API calls
-    if (query === lastFetchedQuery) return;
-    lastFetchedQuery = query;
+    const cacheKey = `${query}__${currentCategory}`;
+    if (cacheKey === lastFetchedQuery) return;
+    lastFetchedQuery = cacheKey;
     currentOffset = 0;
 
+    showSkeletonChips();
+
     try {
-      const res = await fetch(`http://127.0.0.1:8000/suggest?query=${encodeURIComponent(query)}&offset=0&limit=15`);
+      const url = `http://127.0.0.1:8000/suggest?query=${encodeURIComponent(query)}&offset=0&limit=15&category=${currentCategory}`;
+      const res = await fetch(url);
       const data = await res.json();
 
-      // Backend detected gibberish
       if (data.cache === "gibberish") {
         keywordsDiv.innerHTML = '<span class="kw-msg">⚠️ Please enter a meaningful topic.</span>';
         return;
       }
-
-      // No domain found or query skipped
-      if (data.cache === "skip" || data.cache === "no_domain" || !data.suggestions || !data.suggestions.length) {
+      if (!data.suggestions || !data.suggestions.length) {
         keywordsDiv.innerHTML = '<span class="kw-msg">No suggestions found. Try a more specific topic.</span>';
         return;
       }
-
       renderChips(data.suggestions, false);
+    } catch (e) {
+      keywordsDiv.innerHTML = '<span class="kw-msg">Backend not reachable.</span>';
+    }
+  }
+
+  // ── Fetch category results ──
+  async function fetchCategoryResults(query, category) {
+    const resultsBox   = document.getElementById("resultsBox");
+    const resultsGrid  = document.getElementById("resultsGrid");
+    const resultsTitle = document.getElementById("resultsTitle");
+
+    if (category === "general") {
+      resultsBox.classList.remove("visible");
+      return;
+    }
+
+    resultsGrid.innerHTML = "";
+
+    const endpointMap = {
+      videos:   "/search/videos",
+      shopping: "/search/shopping",
+      images:   "/search/images",
+      news:     "/search/news",
+      places:   "/search/places"
+    };
+
+    const endpoint = endpointMap[category];
+    if (!endpoint) return;
+
+    try {
+      const res = await fetch(`http://127.0.0.1:8000${endpoint}?query=${encodeURIComponent(query)}`);
+      const data = await res.json();
+
+      if (data.error || !data.results || !data.results.length) {
+        resultsBox.classList.remove("visible");
+        return;
+      }
+
+      const icons = { shopping:"🛍️", images:"🖼️", videos:"🎬", news:"📰", places:"📍" };
+      resultsTitle.textContent = `${icons[category]} ${CATEGORIES[category].label} results for "${query}"`;
+      resultsBox.classList.add("visible");
+
+      if (category === "videos") {
+        resultsGrid.style.gridTemplateColumns = "repeat(auto-fill, minmax(260px, 1fr))";
+        resultsGrid.innerHTML = data.results.map(v => `
+          <a href="${v.url}" target="_blank" class="video-card">
+            <img src="${v.thumbnail}" alt="${escHtml(v.title)}" onerror="this.style.display='none'"/>
+            <div class="vc-info">
+              <div class="vc-title">${escHtml(v.title)}</div>
+              <div class="vc-channel">📺 ${escHtml(v.channel)}</div>
+              <div class="vc-date">${v.published}</div>
+            </div>
+          </a>`).join("");
+
+      } else if (category === "shopping") {
+        resultsGrid.style.gridTemplateColumns = "repeat(auto-fill, minmax(220px, 1fr))";
+        resultsGrid.innerHTML = data.results.map(s => `
+          <a href="${s.link}" target="_blank" class="shop-card">
+            ${s.image ? `<img src="${s.image}" alt="" onerror="this.style.display='none'"/>` : ""}
+            <div class="sc-title">${escHtml(s.title)}</div>
+            <div class="sc-source">🔗 ${escHtml(s.source)}</div>
+            <div class="sc-snippet">${escHtml(s.snippet)}</div>
+          </a>`).join("");
+
+      } else if (category === "images") {
+        resultsGrid.style.gridTemplateColumns = "repeat(auto-fill, minmax(180px, 1fr))";
+        resultsGrid.innerHTML = data.results.map(img => `
+          <a href="${img.context_link}" target="_blank" class="image-card">
+            <img src="${img.thumbnail}" alt="${escHtml(img.title)}" onerror="this.background='#eee'"/>
+            <div class="ic-title">${escHtml(img.source)}</div>
+          </a>`).join("");
+
+      } else if (category === "news") {
+        resultsGrid.style.gridTemplateColumns = "1fr";
+        resultsGrid.innerHTML = data.results.map(n => `
+          <a href="${n.link}" target="_blank" class="news-card">
+            ${n.image ? `<img src="${n.image}" alt="" onerror="this.style.display='none'"/>` : ""}
+            <div class="nc-info">
+              <div class="nc-title">${escHtml(n.title)}</div>
+              <div class="nc-source">📰 ${escHtml(n.source)}</div>
+              <div class="nc-snippet">${escHtml(n.snippet)}</div>
+            </div>
+          </a>`).join("");
+
+      } else if (category === "places") {
+        resultsGrid.style.gridTemplateColumns = "1fr";
+        resultsGrid.innerHTML = data.results.map(p => `
+          <a href="${p.link}" target="_blank" class="place-card">
+            ${p.image ? `<img src="${p.image}" alt="" onerror="this.style.display='none'"/>` : ""}
+            <div class="pc-info">
+              <div class="pc-title">${escHtml(p.title)}</div>
+              <div class="pc-source">📍 ${escHtml(p.source)}</div>
+              <div class="pc-snippet">${escHtml(p.snippet)}</div>
+            </div>
+          </a>`).join("");
+      }
 
     } catch (e) {
-      keywordsDiv.innerHTML = '<span class="kw-msg">Backend not reachable — is the server running?</span>';
+      resultsBox.classList.remove("visible");
     }
   }
 
   // ── Render chips ──
   function renderChips(items, append) {
-    if (!append) {
-      keywordsDiv.innerHTML = "";
-      currentOffset = 15;
-    } else {
-      const old = document.getElementById("loadMoreBtn");
-      if (old) old.remove();
-    }
-
+    if (!append) { keywordsDiv.innerHTML = ""; currentOffset = 15; }
+    else { const old = document.getElementById("loadMoreBtn"); if (old) old.remove(); }
     if (!items.length && !append) {
       keywordsDiv.innerHTML = '<span class="kw-msg">No suggestions found.</span>';
       return;
     }
-
     items.forEach((k, i) => {
       const chip = document.createElement("span");
       chip.className = "chip" + (selected.includes(k) ? " selected" : "");
-      if (!append && i < 5) chip.classList.add("top"); // top 5 highlighted in gold
+      if (!append && i < 5) chip.classList.add("top");
       chip.innerText = k;
       chip.dataset.kw = k;
       chip.onclick = () => toggleChip(k, chip);
       keywordsDiv.appendChild(chip);
     });
-
-    // Load more button
     const btn = document.createElement("button");
     btn.id = "loadMoreBtn";
     btn.className = "load-more-btn";
@@ -208,7 +449,7 @@ button:disabled{background:#999;cursor:not-allowed}
     keywordsDiv.appendChild(btn);
   }
 
-  // ── Toggle chip selection ──
+  // ── Toggle chip ──
   function toggleChip(k, chipEl) {
     if (selected.includes(k)) {
       selected = selected.filter(x => x !== k);
@@ -223,10 +464,7 @@ button:disabled{background:#999;cursor:not-allowed}
   // ── Render selected keywords ──
   function renderSelected() {
     selectedDiv.innerHTML = "";
-    if (!selected.length) {
-      selectedArea.style.display = "none";
-      return;
-    }
+    if (!selected.length) { selectedArea.style.display = "none"; return; }
     selectedArea.style.display = "block";
     selected.forEach(k => {
       const chip = document.createElement("span");
@@ -234,22 +472,27 @@ button:disabled{background:#999;cursor:not-allowed}
       chip.innerText = k + "  ✕";
       chip.onclick = () => {
         selected = selected.filter(x => x !== k);
-        const kwChip = keywordsDiv.querySelector(`[data-kw="${k}"]`);
-        if (kwChip) kwChip.classList.remove("selected");
+        const kw = keywordsDiv.querySelector(`[data-kw="${k}"]`);
+        if (kw) kw.classList.remove("selected");
         renderSelected();
       };
       selectedDiv.appendChild(chip);
     });
   }
 
-  // ── Load more suggestions ──
+  // ── Load more ──
   async function loadMore() {
     const btn = document.getElementById("loadMoreBtn");
     btn.innerText = "Loading...";
     btn.disabled = true;
 
     try {
-      const res = await fetch(`http://127.0.0.1:8000/suggest?query=${encodeURIComponent(currentQuery)}&offset=${currentOffset}&limit=10`);
+      const existing = Array.from(keywordsDiv.querySelectorAll(".chip"))
+        .map(c => c.dataset.kw).filter(Boolean);
+
+      const res = await fetch(
+        `http://127.0.0.1:8000/suggest/more?query=${encodeURIComponent(currentQuery)}&category=${currentCategory}&existing=${encodeURIComponent(existing.join(","))}`
+      );
       const data = await res.json();
 
       if (!data.suggestions || !data.suggestions.length) {
@@ -257,18 +500,13 @@ button:disabled{background:#999;cursor:not-allowed}
         return;
       }
 
-      const existing = Array.from(keywordsDiv.querySelectorAll(".chip"))
-        .map(c => c.dataset.kw?.toLowerCase()).filter(Boolean);
-      const newItems = data.suggestions.filter(s => !existing.includes(s.toLowerCase()));
-
-      if (!newItems.length) {
-        btn.innerText = "No more suggestions";
-        return;
-      }
+      const newItems = data.suggestions.filter(
+        s => !existing.map(e => e.toLowerCase()).includes(s.toLowerCase())
+      );
 
+      if (!newItems.length) { btn.innerText = "No more suggestions"; return; }
       currentOffset += 10;
       renderChips(newItems, true);
-
     } catch (e) {
       btn.innerText = "+ More";
       btn.disabled = false;
@@ -286,15 +524,16 @@ button:disabled{background:#999;cursor:not-allowed}
     document.getElementById("askBtn").disabled = true;
 
     step++;
-    const chat  = document.getElementById("chat");
+    const chat = document.getElementById("chat");
     const block = document.createElement("div");
     block.className = "chat-block";
+    const catInfo = CATEGORIES[currentCategory];
     block.innerHTML = `
       <div class="go-label">${step}. Go</div>
       <div class="prompt-line"><b>Prompt:</b> ${escHtml(combined)}</div>
+      <div class="cat-line"><b>Category:</b> ${catInfo.icon} ${catInfo.label}</div>
       <div class="kw-line"><b>Keywords:</b> ${selected.length ? selected.map(escHtml).join(", ") : "<i>none selected</i>"}</div>
-      <div class="answer"><i>⏳ AI is generating answer...</i></div>
-    `;
+      <div class="answer"><i>⏳ AI is generating answer...</i></div>`;
     chat.appendChild(block);
     block.scrollIntoView({ behavior: "smooth" });
 
@@ -306,6 +545,7 @@ button:disabled{background:#999;cursor:not-allowed}
         body: JSON.stringify({
           query: combined,
           selected_attributes: selected,
+          category: currentCategory,
           chat_history: chatHistory
         })
       });
@@ -315,46 +555,74 @@ button:disabled{background:#999;cursor:not-allowed}
       answer = "Backend not running. Start the server on port 8000.";
     }
 
+    if (selected.length > 0) {
+      fetch("http://127.0.0.1:8000/feedback", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          query: combined,
+          selected_chips: selected,
+          domain: currentQuery
+        })
+      }).catch(() => {});
+    }
+
     block.querySelector(".answer").innerHTML = `<b>AI Answer</b><br/><br/>${formatAnswer(answer)}`;
-    chatHistory.push({ query: combined, chips: [...selected], answer });
+    chatHistory.push({ query: combined, chips: [...selected], category: currentCategory, answer });
 
-    // Reset after submit
-    promptInput.value = "";
     document.getElementById("extra").value = "";
-    selected = [];
-    lastFetchedQuery = ""; // reset so next query fetches fresh
-    renderSelected();
-    keywordsDiv.innerHTML = '<span class="kw-msg">Start typing to see suggestions...</span>';
-
     document.getElementById("goBtn").disabled = false;
     document.getElementById("askBtn").disabled = false;
     promptInput.focus();
   }
 
-  // ── Clear all ──
+  // ── Clear functions ──
   function clearAll() {
     promptInput.value = "";
     document.getElementById("extra").value = "";
-    selected = [];
-    chatHistory = [];
-    step = 0;
-    lastFetchedQuery = "";
+    selected = []; chatHistory = []; step = 0; lastFetchedQuery = ""; currentCategory = "general";
+    document.querySelectorAll(".cat-tab").forEach(t => t.classList.remove("active"));
+    document.querySelector("[data-cat='general']").classList.add("active");
+    const badge = document.getElementById("categoryBadge");
+    badge.textContent = "General"; badge.className = "category-badge general";
     renderSelected();
     keywordsDiv.innerHTML = '<span class="kw-msg">Start typing to see suggestions...</span>';
+    document.getElementById("resultsBox").classList.remove("visible");
     document.getElementById("chat").innerHTML = "";
   }
 
-  // ── Format AI answer (markdown-like) ──
+  function clearPrompt() {
+    promptInput.value = "";
+    keywordsDiv.innerHTML = '<span class="kw-msg">Start typing...</span>';
+    lastFetchedQuery = "";
+    document.getElementById("resultsBox").classList.remove("visible");
+    promptInput.focus();
+  }
+
+  function clearExtra() { document.getElementById("extra").value = ""; }
+
+  function clearSelected() {
+    selected = [];
+    document.querySelectorAll(".chip.selected").forEach(c => c.classList.remove("selected"));
+    renderSelected();
+  }
+
+  function clearSuggestions() {
+    keywordsDiv.innerHTML = '<span class="kw-msg">Start typing to see suggestions...</span>';
+    lastFetchedQuery = "";
+  }
+
+  // ── Format AI answer ──
   function formatAnswer(text) {
     return text
       .replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;')
-      .replace(/## (.*)/g, '<h2>$1</h2>')
-      .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-      .replace(/\*(.*?)\*/g, '<em>$1</em>')
-      .replace(/---/g, '<hr style="border:none;border-top:1px solid #ddd;margin:10px 0"/>')
-      .replace(/\n\n+/g, '</p><p>')
-      .replace(/\n/g, '<br/>')
-      .replace(/^/, '<p>').replace(/$/, '</p>');
+      .replace(/## (.*)/g,'<h2>$1</h2>')
+      .replace(/\*\*(.*?)\*\*/g,'<strong>$1</strong>')
+      .replace(/\*(.*?)\*/g,'<em>$1</em>')
+      .replace(/---/g,'<hr style="border:none;border-top:1px solid #ddd;margin:10px 0"/>')
+      .replace(/\n\n+/g,'</p><p>')
+      .replace(/\n/g,'<br/>')
+      .replace(/^/,'<p>').replace(/$/,'</p>');
   }
 
   function escHtml(t) {

logs/app.log

@@ -0,0 +1,317 @@
+{"path": "/suggest", "query": "test", "latency_ms": 8392, "status_code": 200, "category": "general", "timestamp": "2026-03-24T07:41:32.768471"}
+{"path": "/suggest", "query": "test", "latency_ms": 253, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:23:27.882782"}
+{"path": "/suggest", "query": "copp", "latency_ms": 9445, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:39:12.819615"}
+{"path": "/suggest", "query": "copper bottels", "latency_ms": 9909, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:39:16.239737"}
+{"path": "/suggest", "query": "copper bott", "latency_ms": 10646, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:39:18.126377"}
+{"path": "/suggest", "query": "copper bottles", "latency_ms": 9745, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:39:18.258745"}
+{"path": "/suggest", "query": "copper bottles", "latency_ms": 30, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:39:36.052442"}
+{"path": "/search/shopping", "query": "copper bottles", "latency_ms": 2, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:40:15.918487"}
+{"path": "/suggest", "query": "copper bottles", "latency_ms": 3217, "status_code": 200, "category": "shopping", "timestamp": "2026-03-24T09:40:19.434479"}
+{"path": "/suggest", "query": "copper bottles", "latency_ms": 4228, "status_code": 200, "category": "shopping", "timestamp": "2026-03-24T09:40:41.432054"}
+{"path": "/search/images", "query": "copper bottles", "latency_ms": 2, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:40:44.661391"}
+{"path": "/suggest", "query": "copper bottles", "latency_ms": 4261, "status_code": 200, "category": "images", "timestamp": "2026-03-24T09:40:49.225755"}
+{"path": "/suggest", "query": "cmf buds", "latency_ms": 13295, "status_code": 200, "category": "general", "timestamp": "2026-03-24T09:54:38.648363"}
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start.bat

@@ -0,0 +1,10 @@
+@echo off
+echo Starting Docker containers...
+docker start decision_pg
+docker start decision_redis
+echo Waiting for PostgreSQL to be ready...
+timeout /t 3
+echo Starting FastAPI server...
+cd C:\Users\rithv\OneDrive\Desktop\decision_engine_project
+call venv\Scripts\activate
+python -m uvicorn app.main:app --host 127.0.0.1 --port 8000