import os
from langchain_core.prompts import PromptTemplate
from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
from langchain_community.document_loaders import PyPDFLoader
import google.generativeai as genai
import gradio as gr

# Function for initialization
def initialize(pdf_file, question):
    try:
        # Access the uploaded file information from Gradio
        file_info = pdf_file

        # Check if a file was uploaded
        if file_info is not None:
            # Construct potential file path based on temporary directory and filename
            file_path = os.path.join("/tmp", file_info.name)  # Adjust temporary directory if needed
            if os.path.exists(file_path):
                # Process the PDF
                pdf_loader = PyPDFLoader(file_path)
                pages = pdf_loader.load_and_split()
                processed_context = "\n".join(str(page.page_content) for page in pages[:30])  # Limit to first 30 pages

                # Configure Google Generative AI (replace with your API key)
                genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

                # Prompt template for formatting context and question
                prompt_template = """Answer the question as precise as possible using the provided context. If the answer is not contained in the context, say "answer not available in context" 

                Context: 
                {context}

                Question: 
                {question} 

                Answer:
                """

                prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])

                # Load the GeminiPro model
                model = genai.GenerativeModel('gemini-pro')

                # Option 1: Using GeminiPro's Text Generation (if applicable)

                # Check if the model has a 'generate' method (or similar) - adjust based on actual method
                if hasattr(model, 'generate'):
                    # Process context and question (already done)

                    # Generate answer using GeminiPro's generate method
                    generated_answer = model.generate(prompt=prompt)  # Replace with the appropriate method

                    # Extract the answer (parse the output from 'generate')
                    # ... (implementation depends on the model's output format)

                    return generated_answer

                # Option 2: Alternative LLM Integration (if GeminiPro methods not suitable)

                # Replace this section with code using an alternative library/framework
                # for question answering (e.g., transformers, haystack)
                # Ensure the code integrates with your chosen LLM and handles context processing,
                # question answering, and answer extraction.

                # Example placeholder (replace with your actual implementation):
                # return "Alternative LLM integration not yet implemented."

            else:
                return "Error: The uploaded file could not be found."
        else:
            return "Error: No PDF file was uploaded."

    except Exception as e:
        return f"An error occurred: {e}"  # Generic error handling

# Create a Gradio interface
interface = gr.Interface(
    fn=initialize,
    inputs=[
        gr.File(label="Upload PDF"),  # No need for 'type' argument
        gr.Textbox(label="Question")
    ],
    outputs="text",
    title="GeminiPro Q&A Bot",
    description="Ask questions about the uploaded PDF document.",
)

# Launch the interface
interface.launch()