import gradio as gr
import langchain.llms as llms
import langchain.llms.prompts as prompts
import langchain.pipelines as pipelines
from langchain.llms.responses import ResponseItem

def process_pdf(pdf_file):
    """Processes the uploaded PDF using a pre-trained information extraction pipeline.

    Args:
        pdf_file (bytes): The uploaded PDF file content.

    Returns:
        dict: A dictionary containing the extracted information from the PDF.
    """

    # Replace with your preferred information extraction pipeline
    # (e.g., Camelot, PyMuPDF, PDFMiner.Six)
    extracted_data = extract_information_from_pdf(pdf_file)
    return extracted_data

def answer_question(question, context, llm):
    """Answers the user's question using the provided context and LLaMA3 model.

    Args:
        question (str): The user's question.
        context (dict): The extracted information from the PDF.
        llm (llms.BaseLLM): The LLaMA3 language model instance.

    Returns:
        ResponseItem: A ResponseItem object containing the answer, score, and retrieval.
    """

    # Replace with your preferred RAG prompt template
    # (e.g., "The document says [RETRIEVAL]. Can you answer this question based on it: {question}?")
    prompt = prompts.get_rag_answering_prompt(question, context, retrieval_template="")

    response = llm.run(prompt, wait_for_sequences=True)
    return response[0]

def chatbot(pdf_file, message, chat_history):
    """Handles user interaction, processes PDF, answers questions, and maintains chat history.

    Args:
        pdf_file (bytes, optional): The uploaded PDF file content (if applicable).
        message (str): The user's message (question).
        chat_history (list): A list of previous messages.

    Returns:
        list: An updated chat history with the chatbot's response.
    """

    if pdf_file is not None:
        context = process_pdf(pdf_file)
        chat_history.append(f"**You uploaded a PDF.**")

    if message:
        # Access the LLaMA3 model (replace with your setup)
        llm = llms.get_llm("facebook/bart-base")  # Example LLaMA3 model (replace with your access)

        response = answer_question(message, context, llm)
        chat_history.append(f"**User:** {message}")
        chat_history.append(f"**Chatbot:** {response.generated_text}")

    return chat_history

# Gradio interface setup
interface = gr.Interface(
    fn=chatbot,
    inputs=[
        gr.File(type="pdf", label="Upload PDF (optional)"),
        gr.Textbox(label="Ask a question"),
    ],
    outputs=gr.Textbox(multiline=True),
    title="PDF Q&A Chatbot with LLaMA3",
    description="Ask questions about the uploaded PDF or provide an empty file to use example content.",
)

interface.launch()