import os
os.environ["POSTHOG_DISABLED"] = "true"  # Disable PostHog telemetry
import requests
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from dotenv import load_dotenv
#from kb_embed import search_knowledge_base
from services.kb_creation import collection, ingest_documents, search_knowledge_base
from contextlib import asynccontextmanager
import google.generativeai as genai


# --- 1. Initialize FastAPI ---
#app = FastAPI()
@asynccontextmanager
async def lifespan(app: FastAPI):
    try:
        folder_path = os.path.join(os.getcwd(), "documents")
        if collection.count() == 0:
                print("🔍 KB empty. Running ingestion...")
                ingest_documents(folder_path)
        else:
                print(f"✅ KB already populated with {collection.count()} entries. Skipping ingestion.")
    except Exception as e:
        print(f"⚠️ KB ingestion failed: {e}")
    yield

app = FastAPI(lifespan=lifespan)

# --- Configure CORS ---
origins = [
    "https://nova-chatbot-chatbot-frontend.hf.space",
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# --- 3. Define the Request Data Structure ---
class ChatInput(BaseModel):
    user_message: str

# --- 4. Gemini API Setup ---
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
GEMINI_URL = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent?key={GEMINI_API_KEY}"

# --- 5. Endpoints ---
@app.get("/")
async def health_check():
    return {"status": "ok"}

         
@app.post("/chat")
async def chat_with_ai(input_data: ChatInput):
    """Handle chat interactions using Google Generative AI via requests."""
    try:
        # Retrieve relevant documents from knowledge base
        kb_results = search_knowledge_base(input_data.user_message, top_k=10)
        #print(f"kb_results are: {kb_results}")

        # Extract relevant context from search results
        context = ""
        relevant_docs=[]
        if kb_results and kb_results.get('documents'):
            # Limit context to avoid token limits - take top 2 most relevant
            relevant_docs = kb_results['documents'][0][:2]
            context = "\n\n".join(relevant_docs)
        
        # Construct enhanced prompt with context
        if context:
            enhanced_prompt = f"""Use the following knowledge base context to answer the user's question accurately.
If the context contains relevant information, base your answer on it. 
If the context doesn't help, say you're raising a ticket and provide a ticket number.
Knowledge Base Context:
{context}
User Question: {input_data.user_message}
Answer:"""
        else:
            enhanced_prompt = f"User Question: {input_data.user_message}\n\nAnswer:"
        headers = {"Content-Type": "application/json"}
        payload = {
            "contents": [
                {
                    "parts": [{"text": enhanced_prompt}]
                }
            ]
        }

        response = requests.post(GEMINI_URL, headers=headers, json=payload, verify=False)  # SSL disabled for testing
        print("Gemini status:", response.status_code)
        print("Gemini resp snippet:", response.text[:500])
        result = response.json()
        #print("result",result)
        # Extract Gemini's response
        bot_response = result["candidates"][0]["content"]["parts"][0]["text"]

        # Include debug info in response
        debug_info = f"Context found: {'Yes' if context else 'No'}"
        if context:
            debug_info += f" (Top {len(relevant_docs)} documents used)"

        return {"bot_response": bot_response, "debug": debug_info}


    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))