Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 from langchain_core.prompts import PromptTemplate
-from langchain.chains.question_answering import load_qa_chain
 from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
 from langchain_community.document_loaders import PyPDFLoader
 import google.generativeai as genai
@@ -42,19 +41,13 @@ def initialize(pdf_file, question):
                 # Load the GeminiPro model
                 model = genai.GenerativeModel('gemini-pro')
 
-                # Debugging: Print object type and attributes
-                print(type(model))
-                print(dir(model))  # List attributes and methods
+                # ... rest of your code for processing context and question
 
-                # Check if the model has a 'generate' method (or similar)
-                if not hasattr(model, 'generate'):
-                    raise Exception("Your LLM object might not have a text generation method!")
+                # Generate answer using GeminiPro's predict method (replace with the appropriate method)
+                generated_answer = model.predict(inputs=prompt)  # Assuming a 'predict' method
 
-                stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-                stuff_answer = stuff_chain(input_data={"context": processed_context, "question": question}, return_only_outputs=True)
-
-                # Extract the answer
-                generated_answer = stuff_answer['output_text']
+                # Extract the answer (parse the output from 'predict')
+                # ... (implementation depends on the model's output format)
 
                 return generated_answer
             else: