app.py

import os
import asyncio
import gradio as gr
from langchain_core.prompts import PromptTemplate
from langchain_core.documents import Document
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
from langchain.chains.question_answering import load_qa_chain
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from PIL import Image
import io
from functools import lru_cache
import concurrent.futures
import pymupdf

# Configure Gemini API
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Load Mistral model (lazy loading)
model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
mistral_tokenizer = None
mistral_model = None

def load_mistral_model():
    global mistral_tokenizer, mistral_model
    if mistral_tokenizer is None or mistral_model is None:
        mistral_tokenizer = AutoTokenizer.from_pretrained(model_path)
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        dtype = torch.bfloat16
        mistral_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)

@lru_cache(maxsize=100)
def get_pdf_content(file_path):
    doc = pymupdf.open(file_path)
    content = []
    for page_num in range(len(doc)):
        page = doc[page_num]
        text = page.get_text()
        content.append(Document(page_content=text, metadata={"page": page_num + 1}))
    return content

async def process_pdf(file_path, question):
    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
    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" \n\n Context: \n {context}?\n Question: \n {question} \n Answer: """
    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    
    pdf_content = get_pdf_content(file_path)
    context = "\n".join([doc.page_content for doc in pdf_content[:5]])  # Limit to first 5 pages for efficiency
    
    stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
    stuff_answer = await stuff_chain.arun({"input_documents": pdf_content[:5], "question": question, "context": context})
    return stuff_answer

async def process_image(image, question):
    model = genai.GenerativeModel('gemini-pro-vision')
    response = await model.generate_content_async([image, question])
    return response.text

async def generate_mistral_followup(answer):
    load_mistral_model()
    mistral_prompt = f"Based on this answer: {answer}\nGenerate a follow-up question:"
    mistral_inputs = mistral_tokenizer.encode(mistral_prompt, return_tensors='pt').to(mistral_model.device)
    with torch.no_grad():
        mistral_outputs = mistral_model.generate(mistral_inputs, max_length=50)
    mistral_output = mistral_tokenizer.decode(mistral_outputs[0], skip_special_tokens=True)
    return mistral_output

async def process_input(file, image, question):
    try:
        if file is not None:
            gemini_answer = await process_pdf(file.name, question)
        elif image is not None:
            gemini_answer = await process_image(image, question)
        else:
            return "Please upload a PDF file or an image."

        mistral_followup = await generate_mistral_followup(gemini_answer)
        combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_followup}"
        return combined_output
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Optimized Multi-modal RAG Knowledge Retrieval using Gemini API and Mistral Model")
    
    with gr.Row():
        with gr.Column():
            input_file = gr.File(label="Upload PDF File")
            input_image = gr.Image(type="pil", label="Upload Image")
        input_question = gr.Textbox(label="Ask about the document or image")
    
    output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")
    
    submit_button = gr.Button("Submit")
    submit_button.click(fn=lambda file, image, question: asyncio.run(process_input(file, image, question)), 
                        inputs=[input_file, input_image, input_question], 
                        outputs=output_text)

if __name__ == "__main__":
    demo.launch()