app.py

import os
import gradio as gr
import traceback

# ---------------- LangChain (STABLE 0.1.x) ----------------
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.retrievers import EnsembleRetriever

# Providers
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings

# Community
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import (
    PyPDFLoader,
    TextLoader,
    Docx2txtLoader
)
from langchain_community.retrievers import BM25Retriever

from langchain_text_splitters import RecursiveCharacterTextSplitter

# ---------------- CONFIG ----------------
GROQ_API_KEY = os.getenv("GROQ_API")

STRICT_PROMPT = PromptTemplate(
    template="""
You are a strict document-based assistant.

Rules:
1. ONLY use the provided context.
2. If the answer is not in the context, say:
"I'm sorry, but the provided documents do not contain information to answer this question."

Context:
{context}

Question: {question}

Answer:
""",
    input_variables=["context", "question"]
)

# ---------------- FILE LOADER ----------------
def load_any(path: str):
    p = path.lower()
    if p.endswith(".pdf"):
        return PyPDFLoader(path).load()
    if p.endswith(".txt"):
        return TextLoader(path, encoding="utf-8").load()
    if p.endswith(".docx"):
        return Docx2txtLoader(path).load()
    return []

# ---------------- BUILD CHAIN ----------------
def process_files(files, response_length):
    if not files:
        return None, "❌ No files uploaded"
    if not GROQ_API_KEY:
        return None, "❌ GROQ_API secret not set"

    try:
        docs = []
        for f in files:
            docs.extend(load_any(str(f)))  # 🔥 THIS IS THE FIX

        splitter = RecursiveCharacterTextSplitter(
            chunk_size=800,
            chunk_overlap=100
        )
        chunks = splitter.split_documents(docs)

        embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )

        faiss_db = FAISS.from_documents(chunks, embeddings)
        faiss_retriever = faiss_db.as_retriever(search_kwargs={"k": 3})

        bm25 = BM25Retriever.from_documents(chunks)
        bm25.k = 3

        retriever = EnsembleRetriever(
            retrievers=[faiss_retriever, bm25],
            weights=[0.5, 0.5]
        )

        llm = ChatGroq(
            groq_api_key=GROQ_API_KEY,
            model="llama-3.3-70b-versatile",
            temperature=0,
            max_tokens=int(response_length)
        )

        memory = ConversationBufferMemory(
            memory_key="chat_history",
            return_messages=True,
            output_key="answer"
        )

        chain = ConversationalRetrievalChain.from_llm(
            llm=llm,
            retriever=retriever,
            memory=memory,
            combine_docs_chain_kwargs={"prompt": STRICT_PROMPT},
            return_source_documents=True,
            output_key="answer"
        )

        return chain, "✅ Chatbot built successfully"

    except Exception as e:
        import traceback
        traceback.print_exc()
        return None, f"❌ {repr(e)}"


# ---------------- CHAT ----------------
def chat_function(message, history, chain):
    if chain is None:
        return "⚠️ Build the chatbot first"

    result = chain.invoke({
        "question": message,
        "chat_history": history
    })

    answer = result["answer"]

    sources = {
        os.path.basename(
            d.metadata.get("source", d.metadata.get("file_path", "unknown"))
        )
        for d in result.get("source_documents", [])
    }

    if sources:
        answer += "\n\n---\n**Sources:** " + ", ".join(sources)

    return answer

# ---------------- UI ----------------
with gr.Blocks() as demo:
    gr.Markdown("Multi-RAG Chatbot")

    chain_state = gr.State(None)

    with gr.Row():
        with gr.Column(scale=1):
            files = gr.File(file_count="multiple", label="Upload Documents")
            tokens = gr.Slider(100, 4000, value=1000, step=100, label="Max Tokens")
            build = gr.Button("Build Chatbot", variant="primary")
            status = gr.Textbox(label="Status", interactive=False)

        with gr.Column(scale=2):
            gr.ChatInterface(
                fn=chat_function,
                additional_inputs=[chain_state]
            )

    build.click(
        process_files,
        inputs=[files, tokens],
        outputs=[chain_state, status]
    )

if __name__ == "__main__":
    demo.launch(theme=gr.themes.Soft())