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| import os | |
| import gradio as gr | |
| import asyncio | |
| 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 | |
| # Gemini PDF QA System | |
| async def initialize_gemini(file_path, question): | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| 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"]) | |
| if 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]) | |
| stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt) | |
| stuff_answer = await stuff_chain.acall({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True) | |
| return stuff_answer['output_text'] | |
| else: | |
| return "Error: Unable to process the document. Please ensure the PDF file is valid." | |
| # Mistral Text Completion | |
| class MistralModel: | |
| def __init__(self): | |
| self.model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base" | |
| self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) | |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| self.dtype = torch.bfloat16 | |
| self.model = AutoModelForCausalLM.from_pretrained(self.model_path, torch_dtype=self.dtype, device_map=self.device) | |
| def generate_text(self, prompt, max_length=50): | |
| inputs = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device) | |
| outputs = self.model.generate(inputs, max_length=max_length) | |
| return self.tokenizer.decode(outputs[0]) | |
| mistral_model = MistralModel() | |
| # Combined function for both models | |
| async def process_input(file, question): | |
| gemini_answer = await initialize_gemini(file.name, question) | |
| mistral_answer = mistral_model.generate_text(question) | |
| return gemini_answer, mistral_answer | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# PDF Question Answering and Text Completion System") | |
| input_file = gr.File(label="Upload PDF File") | |
| input_question = gr.Textbox(label="Ask a question or provide a prompt") | |
| process_button = gr.Button("Process") | |
| output_text_gemini = gr.Textbox(label="Answer - Gemini") | |
| output_text_mistral = gr.Textbox(label="Answer - Mistral") | |
| process_button.click( | |
| fn=process_input, | |
| inputs=[input_file, input_question], | |
| outputs=[output_text_gemini, output_text_mistral] | |
| ) | |
| demo.launch() |