Create app.py

app.py

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+import os
+import gradio as gr
+import asyncio
+from langchain_core.prompts import PromptTemplate
+from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
+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 initialization and PDF QA function
+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[:100])
+        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']
+    else:
+        return "Error: Unable to process the document. Please ensure the PDF file is valid."
+
+# Mistral model initialization
+def initialize_mistral():
+    model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    dtype = torch.bfloat16
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)
+    return tokenizer, model
+
+# Mistral text generation function
+def generate_mistral_text(prompt, tokenizer, model):
+    inputs = tokenizer.encode(prompt, return_tensors='pt').to(model.device)
+    outputs = model.generate(inputs, max_length=100)
+    return tokenizer.decode(outputs[0])
+
+# Initialize Mistral model
+mistral_tokenizer, mistral_model = initialize_mistral()
+
+# Gradio interface function
+async def pdf_qa(file, question):
+    gemini_answer = await initialize_gemini(file.name, question)
+    mistral_prompt = f"Based on this answer: '{gemini_answer}', provide a brief summary:"
+    mistral_summary = generate_mistral_text(mistral_prompt, mistral_tokenizer, mistral_model)
+    return f"Gemini Answer:\n{gemini_answer}\n\nMistral Summary:\n{mistral_summary}"
+
+# Define Gradio Interface
+input_file = gr.File(label="Upload PDF File")
+input_question = gr.Textbox(label="Ask about the document")
+output_text = gr.Textbox(label="Answer and Summary")
+
+# Create Gradio Interface
+gr.Interface(
+    fn=pdf_qa,
+    inputs=[input_file, input_question],
+    outputs=output_text,
+    title="PDF Question Answering System with Gemini and Mistral",
+    description="Upload a PDF file, ask questions about the content, and get answers from Gemini with a summary from Mistral."
+).launch()