app.py

import os
import gradio as gr
from langchain_core.prompts import PromptTemplate
from langchain_community.document_loaders import PyPDFLoader
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 transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image

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

# Load Mistral model
model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
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)

# Load BLIP model for image processing
blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)

def process_image(image):
    # Convert PIL Image to tensor
    inputs = blip_processor(images=image, return_tensors="pt").to(device)
    # Generate caption from image
    caption_ids = blip_model.generate(**inputs)
    caption = blip_processor.decode(caption_ids[0], skip_special_tokens=True)
    return caption

def initialize(file_path, image, question):
    try:
        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"])
        
        context = ""
        
        if file_path and os.path.exists(file_path):
            pdf_loader = PyPDFLoader(file_path)
            pages = pdf_loader.load_and_split()
            context += "\n".join(str(page.page_content) for page in pages[:30])
        
        if image:
            image_context = process_image(image)
            context += f"\nImage Context: {image_context}"
        
        if context:
            stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
            stuff_answer = stuff_chain({"input_documents": [], "question": question, "context": context}, return_only_outputs=True)
            gemini_answer = stuff_answer['output_text']
            
            # Use Mistral model for additional text generation
            mistral_prompt = f"Based on this answer: {gemini_answer}\nGenerate a follow-up question:"
            mistral_inputs = mistral_tokenizer.encode(mistral_prompt, return_tensors='pt').to(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)
            
            combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_output}"
            return combined_output
        else:
            return "Error: No valid context provided. Please upload a valid PDF or image."
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Define Gradio Interface
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")
output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")

def multimodal_qa(file, image, question):
    if file is None and image is None:
        return "Please upload a PDF file or an image first."
    file_path = file.name if file else None
    return initialize(file_path, image, question)

# Create Gradio Interface
gr.Interface(
    fn=multimodal_qa,
    inputs=[input_file, input_image, input_question],
    outputs=output_text,
    title="Multi-modal RAG with Gemini API and Mistral Model",
    description="Upload a PDF or an image and ask questions about the content."
).launch()