Update app.py

app.py

@@ -1,6 +1,5 @@
 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
@@ -9,78 +8,63 @@ 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."
+# Configure Gemini API
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-# Improved 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)
+# 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 generate_text(self, prompt, max_length=200):
-        # Improve the prompt for better context
-        enhanced_prompt = f"Question: {prompt}\n\nAnswer: Let's approach this step-by-step:\n1."
-        inputs = self.tokenizer.encode(enhanced_prompt, return_tensors='pt').to(self.model.device)
-        
-        # Generate with more nuanced parameters
-        outputs = self.model.generate(
-            inputs, 
-            max_length=max_length,
-            num_return_sequences=1,
-            no_repeat_ngram_size=3,
-            top_k=50,
-            top_p=0.95,
-            temperature=0.7,
-            do_sample=True
-        )
-        
-        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+def initialize(file_path, 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"])
+        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 = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
+            gemini_answer = stuff_answer['output_text']
 
-mistral_model = MistralModel()
+            # 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 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
+            combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_output}"
+            return combined_output
+        else:
+            return "Error: Unable to process the document. Please ensure the PDF file is valid."
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Enhanced PDF Question Answering and Text Completion System")
-    
-    input_file = gr.File(label="Upload PDF File (Optional)")
-    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 (PDF-based if file uploaded)")
-    output_text_mistral = gr.Textbox(label="Answer - Mistral (General knowledge)")
+# 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 - Combined Gemini and Mistral")
 
-    process_button.click(
-        fn=process_input,
-        inputs=[input_file, input_question],
-        outputs=[output_text_gemini, output_text_mistral]
-    )
+def pdf_qa(file, question):
+    if file is None:
+        return "Please upload a PDF file first."
+    return initialize(file.name, question)
 
-demo.launch()
+# Create Gradio Interface
+gr.Interface(
+    fn=pdf_qa,
+    inputs=[input_file, input_question],
+    outputs=output_text,
+    title="RAG Knowledge Retrieval using Gemini API and Mistral Model",
+    description="Upload a PDF file and ask questions about the content."
+).launch()