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| 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 PIL import Image | |
| import io | |
| # 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) | |
| 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_loader = PyPDFLoader(file_path) | |
| pages = pdf_loader.load_and_split() | |
| context = "\n".join(str(page.page_content) for page in pages[:200]) | |
| stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) | |
| stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True) | |
| return stuff_answer['output_text'] | |
| def process_image(image, question): | |
| model = genai.GenerativeModel('gemini-pro-vision') | |
| response = model.generate_content([image, question]) | |
| return response.text | |
| def generate_mistral_followup(answer): | |
| mistral_prompt = f"Based on this answer: {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=200) | |
| mistral_output = mistral_tokenizer.decode(mistral_outputs[0], skip_special_tokens=True) | |
| return mistral_output | |
| def process_input(file, image, question): | |
| try: | |
| if file is not None: | |
| gemini_answer = process_pdf(file.name, question) | |
| elif image is not None: | |
| gemini_answer = process_image(image, question) | |
| else: | |
| return "Please upload a PDF file or an image." | |
| mistral_followup = 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)}" | |
| # Define Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 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=process_input, inputs=[input_file, input_image, input_question], outputs=output_text) | |
| demo.launch() |