app.py

# import os
# from fastapi import FastAPI, HTTPException
# from pydantic import BaseModel
# from fastapi.middleware.cors import CORSMiddleware
# from dotenv import load_dotenv

# # --- Modern LangChain Imports (LCEL) ---
# from langchain_groq import ChatGroq
# from langchain_huggingface import HuggingFaceEmbeddings
# from langchain_chroma import Chroma
# from langchain_core.prompts import ChatPromptTemplate
# from langchain.chains import create_retrieval_chain
# from langchain.chains.combine_documents import create_stuff_documents_chain

# # 1. Load Environment
# load_dotenv()

# # 2. Setup App
# app = FastAPI()
# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=["*"],
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# print("Initializing AI Backend...")

# # 3. Setup The Brain
# try:
#     # A. Embeddings
#     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

#     # B. Database
#     db = Chroma(
#         persist_directory="chroma_db", 
#         embedding_function=embeddings
#     )

#     # C. LLM
#     llm = ChatGroq(
#         model="llama-3.1-8b-instant",
#         temperature=0,
#         api_key=os.getenv("GROQ_API_KEY")
#     )

#     # D. The Modern RAG Chain (LCEL Style)
#     # 1. Create the Prompt (UPDATED with Strict Rules & Persona)
#     custom_prompt_template = """
#     You are the Official AI Agent of Ganga Bhumi Club at VIT Bhopal.
#     Your Persona: Warm, friendly, and cultural. You belong to UP/Bihar/Jharkhand culture.
#     Language: Mix 70% English and 30% Hinglish (e.g., use words like "Ji", "Namaskar", "Bhaiya", "Dost").

#     STRICT RULES (DO NOT IGNORE):
#     1. USE ONLY the Context below. Do not use outside knowledge.
#     2. If the user asks about something NOT in the context (like "Who is Modi?" or "Python code"), politely redirect them: "Arre dost, main bas Ganga Bhumi Club ki baatein karta hoon. Club ke baare mein kuch puchiye na!"
#     3. Do not assume or generalize. If specific data (like a phone number) is missing, say: "Maaf kijiye, ye jaankari abhi database mein nahi hai."

#     Context Data:
#     <context>
#     {context}
#     </context>

#     User Question: {input}
#     Your Answer:"""

#     prompt = ChatPromptTemplate.from_template(custom_prompt_template)

#     # 2. Create the Document Chain (Combines retrieved docs into the prompt)
#     document_chain = create_stuff_documents_chain(llm, prompt)

#     # 3. Create the Retrieval Chain (Connects database -> docs -> LLM)
#     retriever = db.as_retriever(search_kwargs={"k": 3})
#     retrieval_chain = create_retrieval_chain(retriever, document_chain)

#     print("✅ AI Backend Ready!")

# except Exception as e:
#     print(f"❌ Error during initialization: {e}")
#     retrieval_chain = None

# # 4. API Endpoint
# class QueryRequest(BaseModel):
#     query: str

# @app.post("/ask")
# async def ask_question(request: QueryRequest):
#     if not retrieval_chain:
#         raise HTTPException(status_code=500, detail="Server failed to initialize AI.")
    
#     try:
#         # Note: In LCEL, the input key is often 'input' instead of 'query'
#         response = retrieval_chain.invoke({"input": request.query})
        
#         # The answer is in the 'answer' key
#         return {"answer": response["answer"]}
    
#     except Exception as e:
#         raise HTTPException(status_code=500, detail=str(e))

# @app.get("/")
# def read_root():
#     return {"status": "Club Bot Server is Running"}
import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from dotenv import load_dotenv

# --- Modern LangChain Imports (LCEL) ---
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

# 1. Load Environment
load_dotenv()

# 2. Setup App
app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

print("Initializing AI Backend...")

# 3. Setup The Brain
try:
    # A. Embeddings
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

    # B. Database
    db = Chroma(
        persist_directory="chroma_db", 
        embedding_function=embeddings
    )

    # C. LLM
    llm = ChatGroq(
        model="llama-3.1-8b-instant",
        temperature=0,
        api_key=os.getenv("GROQ_API_KEY")
    )

    # D. The Modern RAG Chain (LCEL Style)
    # 1. Create the Prompt (UPDATED with Your Professional Persona)
    custom_prompt_template = """
    You are the Official AI Agent of Ganga Bhumi Club (GBC), VIT Bhopal.

    PERSONA:
    You are warm, polite, culturally rooted, and respectful.
    You represent the culture of Uttar Pradesh, Bihar, and Jharkhand.
    Your communication must reflect humility, respect, and dignity.

    LANGUAGE STYLE:
    • 70% English + 30% Hinglish
    • Use respectful words such as: “Ji”, “Namaskar”, “Aap”, “Unka”
    • Avoid casual or slang words like: “tu”, “tum”, “dost”, “arre bhai”
    • Maintain an official yet friendly tone suitable for an academic institution

    STRICT ENFORCEMENT RULES (DO NOT IGNORE):

    1. CONTEXT BOUNDARY RULE  
       You MUST use ONLY the information provided in the Context.
       Do NOT use outside knowledge, assumptions, or general explanations.

    2. EXACT MATCH PRIORITY  
       If the user asks about a person, role, event, or detail that exists in the Context,
       you MUST answer directly and accurately using respectful language.

    3. NO ASSUMPTIONS RULE  
       If any specific information is missing from the Context, respond politely:
       “Maaf kijiye, yeh jaankari abhi hamare database mein uplabdh nahi hai.”

    4. REDIRECTION RULE (ONLY WHEN NECESSARY)  
       If the user asks something completely unrelated to Ganga Bhumi Club
       (for example: politics, coding, general knowledge),
       DO NOT answer the question.

       Politely redirect using this tone:
       “Namaskar Ji, main sirf Ganga Bhumi Club se judi jaankari hi pradan karta hoon.
        Agar aap club, events, ya team ke baare mein kuch puchna chahte hain,
        toh kripya batayein.”

    5. RESPONSE FORMAT RULE  
       • Use short, clear paragraphs or bullet points
       • Be factual, calm, and welcoming
       • Never exaggerate roles or achievements

    Context Data:
    <context>
    {context}
    </context>

    User Question: {input}
    Your Answer:"""

    prompt = ChatPromptTemplate.from_template(custom_prompt_template)

    # 2. Create the Document Chain (Combines retrieved docs into the prompt)
    document_chain = create_stuff_documents_chain(llm, prompt)

    # 3. Create the Retrieval Chain (Connects database -> docs -> LLM)
    retriever = db.as_retriever(search_kwargs={"k": 3})
    retrieval_chain = create_retrieval_chain(retriever, document_chain)

    print("✅ AI Backend Ready!")

except Exception as e:
    print(f"❌ Error during initialization: {e}")
    retrieval_chain = None

# 4. API Endpoint
class QueryRequest(BaseModel):
    query: str

@app.post("/ask")
async def ask_question(request: QueryRequest):
    if not retrieval_chain:
        raise HTTPException(status_code=500, detail="Server failed to initialize AI.")
    
    try:
        # Note: In LCEL, the input key is often 'input' instead of 'query'
        response = retrieval_chain.invoke({"input": request.query})
        
        # The answer is in the 'answer' key
        return {"answer": response["answer"]}
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
def read_root():
    return {"status": "Club Bot Server is Running"}