app.py

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
from cache_manager import CacheManager

# ------------------------------
# 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 with Cache-Control
class CachedStaticFiles(StaticFiles):
    def file_response(self, *args, **kwargs):
        response = super().file_response(*args, **kwargs)
        response.headers["Cache-Control"] = "public, max-age=31536000"
        return response

app.mount("/images", CachedStaticFiles(directory="images"), name="images")

# ------------------------------
# Load Core Components Once
# ------------------------------
print("📌 Loading dataset...")
df = pd.read_csv("salahkar_enhanced.csv")
# Optimization: Create O(1) lookup map
name_to_row = df.set_index("name").to_dict('index')

print("📌 Loading smart recommendation engine...")
engine = SmartRecommender()

print("📌 Loading reranker model...")
reranker = Reranker()

print("📌 Loading intent recognizer...")
intent_detector = IntentClassifier()

print("📌 Initializing Cache Manager...")
cache = CacheManager(capacity=200, ttl_seconds=3600)

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}")

    # 0️⃣ Check Cache
    cached_response = cache.get(query)
    if cached_response:
        return cached_response

    # 1️⃣ Detect intent
    detected_intent = intent_detector.predict_intent(query)
    print(f"🧠 Intent Detected: {detected_intent}")

    # 2️⃣ FAISS + Filter Search
    # engine.recommend returns (results_list, intent)
    # Optimization: Pass detected_intent to avoid re-running classification
    rec_results, _ = engine.recommend(query, k=k, intent=detected_intent)

    # 3️⃣ Prepare results for reranker
    prepared = []
    for item in rec_results:
        name = item["name"]
        domain = item["domain"]
        category = item["category"]
        region = item["region"]
        score = item["score"]
        
        # Optimization: O(1) lookup instead of O(N) dataframe filter
        row = name_to_row.get(name)
        if not row:
            continue
            
        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]
    ]

    final_response = {
        "query": query,
        "intent": detected_intent,
        "results": response
    }

    # 7️⃣ Save to Cache
    cache.set(query, final_response)

    return final_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)