app.py

import os
import shutil
from dotenv import load_dotenv
import gradio as gr

from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage

# Low-cost model
MODEL = "gpt-4o-mini"

load_dotenv(override=True)

chain = None  # global chain (OK for single-user; see note below)


def pick_db_dir() -> str:
    # 1) Allow override via env
    env_dir = os.environ.get("DB_DIR")
    if env_dir:
        return env_dir

    # 2) Prefer persistent mount if available/writable
    data_dir = "/data"
    if os.path.isdir(data_dir) and os.access(data_dir, os.W_OK):
        return os.path.join(data_dir, "vector_db")

    # 3) Fallback to tmp (ephemeral)
    return "/tmp/vector_db"


DB_DIR = pick_db_dir()


def process_pdf(pdf_file):
    try:
        if not os.getenv("OPENAI_API_KEY"):
            raise RuntimeError(
                "OPENAI_API_KEY is not set. Add it to your environment or as a Secret on HF Spaces."
            )

        # Ensure DB dir is writable/exists
        os.makedirs(DB_DIR, exist_ok=True)

        file_path = pdf_file.name  # gr.File gives a temp file with .name path
        loader = PyPDFLoader(file_path)
        pages = loader.load()

        if not pages:
            raise ValueError("No text found in PDF (may be scanned or protected).")

        splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,
            chunk_overlap=50,
        )
        chunks = splitter.split_documents(pages)

        if not chunks:
            raise ValueError("Unable to split PDF into chunks (empty/protected PDF).")

        embeddings = OpenAIEmbeddings()

        # Reset persisted DB each upload (now in a writable location)
        if os.path.exists(DB_DIR):
            shutil.rmtree(DB_DIR, ignore_errors=True)
        os.makedirs(DB_DIR, exist_ok=True)

        vectorstore = Chroma.from_documents(
            documents=chunks,
            embedding=embeddings,
            persist_directory=DB_DIR,
        )

        llm = ChatOpenAI(model=MODEL, temperature=0.2)
        retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

        # Store llm + retriever only (no langchain.chains to avoid langchain_core.memory)
        return {"llm": llm, "retriever": retriever}

    except Exception as e:
        raise RuntimeError(f"PDF processing failed: {str(e)}")


def upload_pdf(file):
    global chain
    if file is None:
        chain = None
        return "Please upload a PDF."
    try:
        chain = process_pdf(file)
        return f"PDF processed. Vector DB at: {DB_DIR}. Ask questions now."
    except RuntimeError as e:
        chain = None
        msg = str(e)
        if "OPENAI_API_KEY" in msg:
            return "Error: OPENAI_API_KEY is not set. Add it to a .env file in this folder (OPENAI_API_KEY=sk-...) or run: export OPENAI_API_KEY=your-key"
        return f"Error: {msg}"


def _gradio_history_to_langchain(history):
    """Convert Gradio chat history to LangChain message list."""
    if not history:
        return []
    lc_messages = []
    for m in history:
        if isinstance(m, dict):
            role, content = m.get("role", ""), m.get("content", "")
        else:
            content = getattr(m, "content", m[0] if len(m) > 0 else "")
            role = getattr(m, "role", m[1] if len(m) > 1 else "assistant")
        if role == "user":
            lc_messages.append(HumanMessage(content=content or ""))
        else:
            lc_messages.append(AIMessage(content=content or ""))
    return lc_messages


def _get_answer_from_message(msg) -> str:
    """Extract text from LLM response (AIMessage or str)."""
    if hasattr(msg, "content"):
        return getattr(msg, "content", "") or ""
    return str(msg) if msg else ""


def ask_question(message, history):
    if chain is None:
        history = history or []
        history.append({"role": "assistant", "content": "Upload the PDF first."})
        return history, history, ""

    chat_history_lc = _gradio_history_to_langchain(history or [])
    llm = chain["llm"]
    retriever = chain["retriever"]

    try:
        # 1) Turn current question + history into a standalone question
        contextualize_prompt = ChatPromptTemplate.from_messages([
            ("system", "Given the chat history and the latest user question, write a single standalone question that can be understood without the chat history. If the question is already standalone, return it unchanged. Do not answer the question."),
            MessagesPlaceholder("chat_history"),
            ("human", "{input}"),
        ])
        contextualize_chain = contextualize_prompt | llm
        standalone = contextualize_chain.invoke({"input": message, "chat_history": chat_history_lc})
        query = _get_answer_from_message(standalone).strip() or message

        # 2) Retrieve docs
        docs = retriever.invoke(query)
        context = "\n\n".join((getattr(d, "page_content", "") or str(d) for d in docs))

        # 3) Answer with context + history
        qa_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an assistant for question-answering. Use the following context to answer. If the answer is not in the context, say so. Be concise.\n\nContext:\n{context}"),
            MessagesPlaceholder("chat_history"),
            ("human", "{input}"),
        ])
        qa_chain = qa_prompt | llm
        response = qa_chain.invoke({"input": message, "chat_history": chat_history_lc, "context": context})
        answer = _get_answer_from_message(response) or "No answer found."
    except Exception as e:
        answer = f"Error: {str(e)}"

    history = history or []
    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": answer})
    return history, history, ""


with gr.Blocks() as demo:
    gr.Markdown("## Chat with your PDF")

    file_input = gr.File(label="Upload your PDF", file_types=[".pdf"])
    status = gr.Textbox(label="Status", interactive=False)

    chatbot = gr.Chatbot(label="Chat history", type="messages")
    msg = gr.Textbox(label="Ask anything related to the PDF...")
    clear = gr.Button("Clear chat")

    state = gr.State([])

    file_input.change(upload_pdf, inputs=[file_input], outputs=[status])
    msg.submit(ask_question, inputs=[msg, state], outputs=[chatbot, state, msg])
    clear.click(lambda: ([], []), inputs=None, outputs=[chatbot, state])

demo.launch(inline=False)