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
@@ -19,44 +18,48 @@ 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)
 
-async def initialize(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"])
-    
-    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']
+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"])
         
-        # 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_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."
+        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']
+            
+            # 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_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)}"
 
 # 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")
 
-async def pdf_qa(file, question):
-    answer = await initialize(file.name, question)
-    return answer
+def pdf_qa(file, question):
+    if file is None:
+        return "Please upload a PDF file first."
+    return initialize(file.name, question)
 
 # Create Gradio Interface
 gr.Interface(