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from fastapi import FastAPI, Query
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
from filtered_search_engine import SmartRecommender
from reranker import Reranker
from intent_classifier import IntentClassifier
from keyword_boosting_layer import apply_keyword_boost
# ------------------------------
# Initialize App
# ------------------------------
app = FastAPI(
title="Salahkar AI Recommender",
description="Smart cultural, heritage & food recommendation engine for BharatVerse",
version="1.0.0"
)
from fastapi.staticfiles import StaticFiles
# CORS Support (allows frontend browser access)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Mount images folder to serve static files
app.mount("/images", StaticFiles(directory="images"), name="images")
# ------------------------------
# Load Core Components Once
# ------------------------------
print("📌 Loading dataset...")
df = pd.read_csv("salahkar_enhanced.csv")
print("📌 Loading smart recommendation engine...")
engine = SmartRecommender()
print("📌 Loading reranker model...")
reranker = Reranker()
print("📌 Loading intent recognizer...")
intent_detector = IntentClassifier()
print("🚀 Salahkar AI Ready!")
# ------------------------------
# Routes
# ------------------------------
@app.get("/")
def root():
return {
"message": "🇮🇳 Welcome to Salahkar AI – BharatVerse Intelligent Recommendation System",
"usage": "/recommend?query=your text"
}
@app.get("/recommend")
def get_recommendation(query: str = Query(..., description="User's search text"), k: int = 7):
print(f"\n🔍 User Query: {query}")
# 1️⃣ Detect intent
detected_intent = intent_detector.predict_intent(query)
print(f"🧠 Intent Detected: {detected_intent}")
# 2️⃣ FAISS + Filter Search
results = engine.recommend(query, k=k)
# 3️⃣ Prepare results for reranker
prepared = []
for name, domain, category, region, score in results:
row = df[df["name"] == name].iloc[0]
prepared.append({
"name": name,
"domain": domain,
"category": category,
"region": region,
"embedding_score": float(score),
"text": row["search_embedding_text"],
"image": row["image_file"]
})
# 4️⃣ Re-rank using cross encoder
reranked_results = reranker.rerank(query, prepared)
# 5️⃣ Apply keyword boosting
final_results = apply_keyword_boost(query, reranked_results)
# 6️⃣ Format response for frontend
response = [
{
"name": item["name"],
"category": item["category"],
"domain": item["domain"],
"region": item["region"],
"score": float(item["final_score"]),
"image": f"/images/{item['image']}" if item.get("image") else None
}
for item in final_results[:k]
]
return {
"query": query,
"intent": detected_intent,
"results": response
}
# -------------------------------------------
# Run (Ignored by HuggingFace — needed only for local testing)
# -------------------------------------------
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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