From 50263c0d07973ee26f8a2b701cd028d157fa0edd Mon Sep 17 00:00:00 2001 From: ashok Date: Fri, 10 Apr 2026 13:09:53 +0530 Subject: [PATCH] Changes ui updated --- .dockerignore | 12 + .env | 5 +- .gitignore | 29 +- Dockerfile | 30 + app/__pycache__/bootstrap.cpython-312.pyc | Bin 8341 -> 11753 bytes app/__pycache__/db_schema.cpython-312.pyc | Bin 2382 -> 3113 bytes .../embedding_cache.cpython-312.pyc | Bin 1304 -> 1308 bytes app/__pycache__/main.cpython-312.pyc | Bin 11621 -> 42893 bytes app/__pycache__/normalizer.cpython-312.pyc | Bin 1456 -> 1456 bytes .../semantic_cache.cpython-312.pyc | Bin 4536 -> 4764 bytes app/__pycache__/vertex_client.cpython-312.pyc | Bin 993 -> 1368 bytes app/analyze_logs.py | 64 ++ app/bootstrap.py | 158 ++-- app/check_db.py | 4 +- app/db_schema.py | 22 +- app/embedding_cache.py | 2 +- app/insert_test.py | 20 + app/log_cleanup.py | 15 + app/logger.py | 20 + app/main.py | 813 +++++++++++++++--- app/semantic_cache.py | 5 +- app/service-account.json | 13 - app/vertex_client.py | 17 +- docker-compose.yml | 46 + index.html | 482 ++++++++--- logs/app.log | 317 +++++++ start.bat | 10 + 27 files changed, 1793 insertions(+), 291 deletions(-) create mode 100644 .dockerignore create mode 100644 Dockerfile create mode 100644 app/analyze_logs.py create mode 100644 app/insert_test.py create mode 100644 app/log_cleanup.py create mode 100644 app/logger.py delete mode 100644 app/service-account.json create mode 100644 docker-compose.yml create mode 100644 logs/app.log create mode 100644 start.bat diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..8f45822f --- /dev/null +++ b/.dockerignore @@ -0,0 +1,12 @@ +.env +.env.* +venv/ +seed/ +.venv/ +__pycache__/ +*.pyc +*.pyo +*.pyd +.git +.gitignore +app/service-account.json \ No newline at end of file diff --git a/.env b/.env index a75eff72..eee1bcd8 100644 --- a/.env +++ b/.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 \ No newline at end of file +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 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 5ddee301..c245931a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,32 @@ +# Virtual environments venv/ seed/ +.venv/ + +# Environment & secrets .env +.env.* +app/service-account.json + +# Python cache __pycache__/ -*.pyc \ No newline at end of file +*.pyc +*.pyo +*.pyd + +# Build artifacts +dist/ +build/ +*.egg-info/ + +# IDE +.vscode/ +.idea/ + +# OS +.DS_Store +Thumbs.db + +# Docker volumes +pg_data/ +redis_data/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..0e4104e8 --- /dev/null +++ b/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"] diff --git a/app/__pycache__/bootstrap.cpython-312.pyc b/app/__pycache__/bootstrap.cpython-312.pyc index 05ddbc9781045fc7607514f320191532f730d956..74f87a3548078c85c8f7728019d137465873721d 100644 GIT binary patch literal 11753 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zKr$;CiXwnikrt5n#bJ}1pHiBWYFCsznMWuaHaTBNlLe#$ E02s1Hwg3PC diff --git a/app/__pycache__/vertex_client.cpython-312.pyc b/app/__pycache__/vertex_client.cpython-312.pyc index 82d9a23d43fecc1d4c496477716a2863915ca81c..cc9cf1006dba1fbb156814df4104bd378d25a6d2 100644 GIT binary patch delta 756 zcmaFJeuIneG%qg~0}!}R-jx}^JdsaAM-9lE&XB^8!kEL5%NWJT2x2qkFhwzgX_hFK z6y_F&DAr0QO_qs%l3~dp?J&RsWH1Bq=T(d#Q%Zm&1Y|M6SO_|WaWz!FmZ65BNWKIv zmBljgp|CzXoDXNzFu=4FGo>)qGN5U(#;=91gcD{`3Udv^Y=*f^FyopSCz~_MyYs{3 z5p)eh3QIcU8rIcNAJj6|FlGrN%*-Tt2qFSXKoLW?t znVhPSn4FwnnpdKbmYI{Pke3f+0J$j&i6vGFllL)N>+#=W$}hgfmY!OYnpbvNtOVpuXGmd4Va#F3WsG8E1hJWNn4*|dn4?%y zSXvmOSSy({Szm&*X)@m8FHS8g%S=v3Qs|uLBpV?;e1SS!=WRMydU;#3i zf%x+aATgbxgkfT*j7t_POd3I#u))|VjOmPPm{v1E__Yi*3|X8oIRrg9kWrqIZ{k5= zMgb5ZJoz)DsuV+(2toyn70jT?>^J!&lQkpvWFuxhQITNRpfFEo*LX)~Xa7*Yka#yw zAJ@t8%(lXex44swQd3g%N-`63ii;V6CQRPLoTMlOWUvEqaSV`XV7McscwI>MqLA)& zA@_?y?sqt3CU>#4_=1A8hyz4`)URYH5(cpafy6Hko80`A(wtPgB5@!WWNxt-kodsN a$jJDVLGw0)>I3nt3k)KkS=bn5!TJDkn^Tnl diff --git a/app/analyze_logs.py b/app/analyze_logs.py new file mode 100644 index 00000000..4423c752 --- /dev/null +++ b/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("\nšŸ“Š BASIC METRICS") +print("=" * 30) + +if total_requests > 0: + print(f"Total Requests: {total_requests}") + print(f"Avg Latency: {total_latency // total_requests} ms") + +print("\nšŸ”„ Top Queries:") +for q, count in sorted(query_count.items(), key=lambda x: x[1], reverse=True)[:5]: + print(f"{q} ā†’ {count} times") + +print("\nāš ļø Slow Requests (>1000ms):") +for q, lat in slow_requests[:5]: + print(f"{q} ā†’ {lat} ms") + +cache_hits = 0 + +if log.get("latency_ms", 0) < 500: + cache_hits += 1 + +print(f"\nāš” 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 \ No newline at end of file diff --git a/app/bootstrap.py b/app/bootstrap.py index e5f13dea..3f8fbed5 100644 --- a/app/bootstrap.py +++ b/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 + # āœ… Reject attributes containing blocked terms + a_lower = a.lower() + if any(b in a_lower for b in blocked): + print(f"āŒ 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 ā€” 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 ā†’ most compared ā†’ 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() + + print("Status Code:", res.status_code) + if res.status_code != 200: + print("āŒ ERROR RESPONSE:") + print(res.text) + raise RuntimeError("Vertex API failed") + + data_json = res.json() + print("āœ… 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") + + # āœ… Quality gate ā€” 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") + # āœ… Duplicate domain detection ā€” 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"āš ļø Similar domain already exists: '{existing.name}' (distance: {existing.distance:.3f}) ā€” skipping bootstrap") + return # āœ… 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}") \ No newline at end of file + print(f"āœ… Domain bootstrapped: {query} ({total_attrs} attributes)") \ No newline at end of file diff --git a/app/check_db.py b/app/check_db.py index 4d0eb7d1..7ed30636 100644 --- a/app/check_db.py +++ b/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) diff --git a/app/db_schema.py b/app/db_schema.py index 3f2ff112..05cf8157 100644 --- a/app/db_schema.py +++ b/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")) @@ -41,5 +38,24 @@ 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 \ No newline at end of file diff --git a/app/embedding_cache.py b/app/embedding_cache.py index 437705c7..ba288e6a 100644 --- a/app/embedding_cache.py +++ b/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) diff --git a/app/insert_test.py b/app/insert_test.py new file mode 100644 index 00000000..db93c632 --- /dev/null +++ b/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") diff --git a/app/log_cleanup.py b/app/log_cleanup.py new file mode 100644 index 00000000..0f01cd7e --- /dev/null +++ b/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() \ No newline at end of file diff --git a/app/logger.py b/app/logger.py new file mode 100644 index 00000000..9c7368d2 --- /dev/null +++ b/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"āŒ Logging failed: {e}") + \ No newline at end of file diff --git a/app/main.py b/app/main.py index 58bacd90..781136b7 100644 --- a/app/main.py +++ b/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" # ā”€ā”€ Startup ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ ensure_schema(engine) create_index_if_not_exists() -print("āœ… System ready") +print("System ready") + + +# ā”€ā”€ Redis helper ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +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()}" # ā”€ā”€ Async cache writer ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ @@ -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 +# ā”€ā”€ Middleware ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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 + + +# ā”€ā”€ Fast Gemini call ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +def call_gemini_fast(query: str, limit: int = 15) -> list: + prompt = f"""List {limit} specific evaluation criteria for: "{query}" +Rules: +- Specific to "{query}" only ā€” 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"āœ… Fast Gemini: {len(suggestions)} suggestions for '{query}'") + return suggestions + except http_requests.exceptions.Timeout: + print(f"ā° Gemini timeout attempt {attempt+1}") + except Exception as e: + print(f"āš ļø Gemini attempt {attempt+1}: {type(e).__name__}") + time.sleep(1) + return [] + + +def call_gemini_more(query: str, existing: list) -> list: + """Second background call ā€” 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"āš ļø call_gemini_more failed: {type(e).__name__}") + return [] + +# ā”€ā”€ Background store in Redis + DB ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +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"šŸ’¾ Redis cached: '{query}' ({len(suggestions)} items)") + except Exception: + pass + try: + bootstrap_domain(query) + print(f"āœ… DB bootstrapped: '{query}'") + if r and cache_key: + try: + r.delete(cache_key) + print(f"šŸ—‘ļø Redis cleared ā†’ DB now serves '{query}'") + except Exception: + pass + except Exception as e: + print(f"āš ļø Bootstrap failed: {type(e).__name__}") + + threading.Thread(target=_store, daemon=True).start() + + +# ā”€ā”€ Main get suggestions function ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +def get_suggestions_fast(query: str, limit: int = 15, existing: list = None) -> dict: + existing = existing or [] + + # ā”€ā”€ L1: Redis cache ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ + 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"āš” Redis HIT: '{query}' ā†’ {len(new_only)} available") + return {"suggestions": new_only[:limit], "cache": "redis_hit"} + except Exception: + pass + + # ā”€ā”€ L2: DB (pgvector) ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ + 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"āš” DB HIT: '{query}' ā†’ {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"āš ļø DB check failed: {type(e).__name__}") + + # ā”€ā”€ L3: Gemini fast (new query) ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ + print(f"šŸ”„ New query ā€” 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"āœ… 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"} + + +# ā”€ā”€ Category suggestions via Gemini ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +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"āš ļø 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"āŒ Category suggestions failed: {type(e).__name__}") + return {"suggestions": [], "cache": "error"} + return {"suggestions": [], "cache": "failed"} + + +# ā”€ā”€ serve_from_db ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +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] + + # ā”€ā”€ /suggest endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ @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"šŸ“ Normalized: '{query}' ā†’ '{normalized}'") - - embedding = get_or_encode(normalized, model) - word_count = len(query.strip().split()) + # Category ā†’ 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"āœ… 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"āœ… Bootstrapped: {query}") - except Exception as e: - print(f"āš ļø 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"} - # āœ… 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}' ā†’ '{normalized}'") + + # ā”€ā”€ Semantic cache check ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ + 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 ā†’ 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("āœ… 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("āš” Medium match ā†’ 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("āŒ 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"āš” DB HIT: '{query}' ā†’ {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"āš ļø DB/cache check failed: {type(e).__name__}") + + # ā”€ā”€ Fast Gemini fallback ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ + print(f"šŸ”„ Fast Gemini for: '{query}'") + result = get_suggestions_fast(query, limit=40) + return build_response(result, result.get("cache", "gemini_fast")) + + +# ā”€ā”€ /suggest/more endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āš” 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"āŒ suggest/more failed: {type(e).__name__}") + return {"suggestions": [], "cache": "error"} + + +# ā”€ā”€ /search/videos endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āŒ YouTube search failed: {type(e).__name__}") + return {"results": [], "error": "Search unavailable. Please try again."} + + +# ā”€ā”€ /search/shopping endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āŒ Shopping search failed: {type(e).__name__}") + return {"results": [], "error": "Search unavailable. Please try again."} + + +# ā”€ā”€ /search/images endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āŒ Image search failed: {type(e).__name__}") + return {"results": [], "error": "Search unavailable. Please try again."} + + +# ā”€ā”€ /search/news endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āŒ News search failed: {type(e).__name__}") + return {"results": [], "error": "Search unavailable. Please try again."} + + +# ā”€ā”€ /search/places endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +@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"āŒ Places search failed: {type(e).__name__}") + return {"results": [], "error": "Search unavailable. Please try again."} # ā”€ā”€ /generate endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ 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"šŸ”„ 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"ā³ Rate limited, waiting {wait}s...") time.sleep(wait) continue - print(f"āœ… 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"ā° 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"āŒ Error: {e}") + print(f"āŒ Error: {type(e).__name__}") return {"answer": "Error getting response. Please try again."} - return {"answer": "Could not get response after 3 attempts. Please try again."} \ No newline at end of file + return {"answer": "Could not get response after 3 attempts. Please try again."} + + +# ā”€ā”€ /feedback endpoint ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ +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"} \ No newline at end of file diff --git a/app/semantic_cache.py b/app/semantic_cache.py index 5060dc7f..354acd63 100644 --- a/app/semantic_cache.py +++ b/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 diff --git a/app/service-account.json b/app/service-account.json deleted file mode 100644 index 26b79df9..00000000 --- a/app/service-account.json +++ /dev/null @@ -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" -} diff --git a/app/vertex_client.py b/app/vertex_client.py index 93e38fb9..2327d026 100644 --- a/app/vertex_client.py +++ b/app/vertex_client.py @@ -1,13 +1,22 @@ # 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()) - return credentials.token \ No newline at end of file + return credentials.token \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..1081a965 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,46 @@ +version: "3.9" + +services: + + postgres: + image: ankane/pgvector # ā† 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: \ No newline at end of file diff --git a/index.html b/index.html index 39b256ae..261f4684 100644 --- a/index.html +++ b/index.html @@ -2,84 +2,200 @@ -AI Prompt Compressor Engine +AI Prompt Composer Engine -
-

AI Prompt Compressor Engine

+

AI Prompt Composer Engine

+
+
+
- + +
+
- Selected Keywords +
+ Selected Keywords + +
+ +
+
šŸ” General
+
šŸ›ļø Shopping
+
šŸ–¼ļø Images
+
šŸŽ¬ Videos
+
šŸ“° News
+
šŸ“ Places
+
+ +
- Suggestions +
+ Suggestions +
+ General + +
+
Start typing to see suggestions...
+ +
+
+ +
+
+
+ +
- +
@@ -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"); - // ā”€ā”€ Gibberish detector ā”€ā”€ - 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: "šŸ”" }, + shopping: { label: "Shopping", badge: "shopping", icon: "šŸ›ļø" }, + images: { label: "Images", badge: "images", icon: "šŸ–¼ļø" }, + videos: { label: "Videos", badge: "videos", icon: "šŸŽ¬" }, + news: { label: "News", badge: "news", icon: "šŸ“°" }, + places: { label: "Places", badge: "places", icon: "šŸ“" } + }; - if (word.match(/[^aeiou]{6,}/)) { gibberishCount++; continue; } // raised to 6 + // ā”€ā”€ Gibberish check ā”€ā”€ + 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; } + // ā”€ā”€ Select category ā”€ā”€ + 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 ${cat}`; + lastFetchedQuery = ""; - if (/[a-z]\d{3,}[a-z]|[0-9]{4,}[a-z]/.test(word)) { gibberishCount++; continue; } + const q = promptInput.value.trim(); + if (q.length >= 3 && !isGibberish(q)) { + keywordsDiv.innerHTML = ``; + clearTimeout(debounceTimer); + debounceTimer = setTimeout(() => fetchKeywords(q), 300); + if (cat !== "general") fetchCategoryResults(q, cat); + else document.getElementById("resultsBox").classList.remove("visible"); + } else { + document.getElementById("resultsBox").classList.remove("visible"); + } } - return gibberishCount > words.length / 2; -} - - // ā”€ā”€ Listen to typing ā”€ā”€ + // ā”€ā”€ Typing listener ā”€ā”€ promptInput.addEventListener("input", () => { clearTimeout(debounceTimer); const q = promptInput.value.trim(); currentQuery = q; - if (q.length < 3) { + if (q.length < 4) { keywordsDiv.innerHTML = 'Keep typing...'; + document.getElementById("resultsBox").classList.remove("visible"); return; } - if (isGibberish(q)) { keywordsDiv.innerHTML = 'āš ļø Please enter a meaningful topic.'; return; } - keywordsDiv.innerHTML = ` - - `; + if (!keywordsDiv.querySelector('.chip')) { + showSkeletonChips(); + } - debounceTimer = setTimeout(() => fetchKeywords(q), 500); + debounceTimer = setTimeout(() => { + fetchKeywords(q); + if (currentCategory !== "general") fetchCategoryResults(q, currentCategory); + }, 1000); }); + // ā”€ā”€ Skeleton chips ā”€ā”€ + function showSkeletonChips() { + keywordsDiv.innerHTML = ""; + for (let i = 0; i < 8; i++) { + const sk = document.createElement("span"); + sk.className = "chip skeleton"; + sk.style.width = `${60 + Math.random() * 60}px`; + sk.innerHTML = " "; + keywordsDiv.appendChild(sk); + } + } + // ā”€ā”€ Fetch suggestions ā”€ā”€ async function fetchKeywords(query) { - // Skip if same query already fetched ā€” 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=${currentCategory}`; + const res = await fetch(url); const data = await res.json(); - // Backend detected gibberish if (data.cache === "gibberish") { keywordsDiv.innerHTML = 'āš ļø Please enter a meaningful topic.'; return; } - - // No domain found or query skipped - if (data.cache === "skip" || data.cache === "no_domain" || !data.suggestions || !data.suggestions.length) { + if (!data.suggestions || !data.suggestions.length) { keywordsDiv.innerHTML = 'No suggestions found. Try a more specific topic.'; return; } - renderChips(data.suggestions, false); + } catch (e) { + keywordsDiv.innerHTML = 'Backend not reachable.'; + } + } + + // ā”€ā”€ Fetch category results ā”€ā”€ + async function fetchCategoryResults(query, category) { + const resultsBox = document.getElementById("resultsBox"); + const resultsGrid = document.getElementById("resultsGrid"); + const resultsTitle = document.getElementById("resultsTitle"); + + if (category === "general") { + resultsBox.classList.remove("visible"); + return; + } + + resultsGrid.innerHTML = ""; + + const endpointMap = { + videos: "/search/videos", + shopping: "/search/shopping", + images: "/search/images", + news: "/search/news", + places: "/search/places" + }; + + const endpoint = endpointMap[category]; + if (!endpoint) return; + + try { + const res = await fetch(`http://127.0.0.1:8000${endpoint}?query=${encodeURIComponent(query)}`); + const data = await res.json(); + + if (data.error || !data.results || !data.results.length) { + resultsBox.classList.remove("visible"); + return; + } + + const icons = { shopping:"šŸ›ļø", images:"šŸ–¼ļø", videos:"šŸŽ¬", news:"šŸ“°", places:"šŸ“" }; + resultsTitle.textContent = `${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 => ` + + ${escHtml(v.title)} +
+
${escHtml(v.title)}
+
šŸ“ŗ ${escHtml(v.channel)}
+
${v.published}
+
+ `).join(""); + + } else if (category === "shopping") { + resultsGrid.style.gridTemplateColumns = "repeat(auto-fill, minmax(220px, 1fr))"; + resultsGrid.innerHTML = data.results.map(s => ` + + ${s.image ? `` : ""} +
${escHtml(s.title)}
+
šŸ”— ${escHtml(s.source)}
+
${escHtml(s.snippet)}
+ `).join(""); + + } else if (category === "images") { + resultsGrid.style.gridTemplateColumns = "repeat(auto-fill, minmax(180px, 1fr))"; + resultsGrid.innerHTML = data.results.map(img => ` + + ${escHtml(img.title)} +
${escHtml(img.source)}
+ `).join(""); + + } else if (category === "news") { + resultsGrid.style.gridTemplateColumns = "1fr"; + resultsGrid.innerHTML = data.results.map(n => ` + + ${n.image ? `` : ""} +
+
${escHtml(n.title)}
+
šŸ“° ${escHtml(n.source)}
+
${escHtml(n.snippet)}
+
+ `).join(""); + + } else if (category === "places") { + resultsGrid.style.gridTemplateColumns = "1fr"; + resultsGrid.innerHTML = data.results.map(p => ` + + ${p.image ? `` : ""} +
+
${escHtml(p.title)}
+
šŸ“ ${escHtml(p.source)}
+
${escHtml(p.snippet)}
+
+ `).join(""); + } } catch (e) { - keywordsDiv.innerHTML = 'Backend not reachable ā€” is the server running?'; + resultsBox.classList.remove("visible"); } } // ā”€ā”€ Render chips ā”€ā”€ 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 = 'No suggestions found.'; 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); } - // ā”€ā”€ Toggle chip selection ā”€ā”€ + // ā”€ā”€ Toggle chip ā”€ā”€ 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} // ā”€ā”€ Render selected keywords ā”€ā”€ 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 + " āœ•"; 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="${k}"]`); + if (kw) kw.classList.remove("selected"); renderSelected(); }; selectedDiv.appendChild(chip); }); } - // ā”€ā”€ Load more suggestions ā”€ā”€ + // ā”€ā”€ Load more ā”€ā”€ async function loadMore() { const btn = document.getElementById("loadMoreBtn"); btn.innerText = "Loading..."; btn.disabled = true; try { - const res = await fetch(`http://127.0.0.1:8000/suggest?query=${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 = `
${step}. Go
Prompt: ${escHtml(combined)}
+
Category: ${catInfo.icon} ${catInfo.label}
Keywords: ${selected.length ? selected.map(escHtml).join(", ") : "none selected"}
-
ā³ AI is generating answer...
- `; +
ā³ AI is generating answer...
`; 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 = `AI Answer

${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 = 'Start typing to see suggestions...'; - document.getElementById("goBtn").disabled = false; document.getElementById("askBtn").disabled = false; promptInput.focus(); } - // ā”€ā”€ Clear all ā”€ā”€ + // ā”€ā”€ Clear functions ā”€ā”€ 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 = 'Start typing to see suggestions...'; + document.getElementById("resultsBox").classList.remove("visible"); document.getElementById("chat").innerHTML = ""; } - // ā”€ā”€ Format AI answer (markdown-like) ā”€ā”€ + function clearPrompt() { + promptInput.value = ""; + keywordsDiv.innerHTML = 'Start typing...'; + 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 = 'Start typing to see suggestions...'; + lastFetchedQuery = ""; + } + + // ā”€ā”€ Format AI answer ā”€ā”€ function formatAnswer(text) { return text .replace(/&/g,'&').replace(//g,'>') - .replace(/## (.*)/g, '

$1

') - .replace(/\*\*(.*?)\*\*/g, '$1') - .replace(/\*(.*?)\*/g, '$1') - .replace(/---/g, '
') - .replace(/\n\n+/g, '

') - .replace(/\n/g, '
') - .replace(/^/, '

').replace(/$/, '

'); + .replace(/## (.*)/g,'

$1

') + .replace(/\*\*(.*?)\*\*/g,'$1') + .replace(/\*(.*?)\*/g,'$1') + .replace(/---/g,'
') + .replace(/\n\n+/g,'

') + .replace(/\n/g,'
') + .replace(/^/,'

').replace(/$/,'

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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 \ No newline at end of file