Update app.py

app.py

@@ -3,50 +3,59 @@ 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
 
+# Configure Gemini API
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
 # Load Mistral model
 model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
+mistral_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)
+mistral_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)
 
 async def initialize(file_path, question):
-    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: """
+    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)
+        gemini_answer = stuff_answer['output_text']
         
-        # Prepare input for Mistral model
-        input_text = prompt.format(context=context, question=question)
-        inputs = tokenizer.encode(input_text, return_tensors='pt').to(device)
-        
-        # Generate the output
+        # 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():
-            outputs = model.generate(inputs, max_length=500)  # Adjust max_length as needed
+            mistral_outputs = mistral_model.generate(mistral_inputs, max_length=50)
+        mistral_output = mistral_tokenizer.decode(mistral_outputs[0], skip_special_tokens=True)
         
-        # Decode and return the output
-        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return 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."
 
 # 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 - Mistral Model")
+output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")
 
-def pdf_qa(file, question):
-    if file is None:
-        return "Please upload a PDF file first."
-    
-    loop = asyncio.get_event_loop()
-    answer = loop.run_until_complete(initialize(file.name, question))
+async def pdf_qa(file, question):
+    answer = await initialize(file.name, question)
     return answer
 
 # Create Gradio Interface
@@ -54,6 +63,6 @@ gr.Interface(
     fn=pdf_qa,
     inputs=[input_file, input_question],
     outputs=output_text,
-    title="RAG Knowledge Retrieval using Mistral Model",
+    title="RAG Knowledge Retrieval using Gemini API and Mistral Model",
     description="Upload a PDF file and ask questions about the content."
 ).launch()