Update app.py

app.py

@@ -70,46 +70,5 @@ def create_vector_store(text):
 st.title("RAG-based Application with Focused Context")
 
 # Predefined Google Drive link
-drive_url = "https://drive.google.com/file/d/1XvqA1OIssRs2gbmOtKFKj-02yQ5X2yg0/view?usp=sharing"
+drive_url = "h
 
-# Extract document content
-st.write("Extracting content from the document...")
-text = extract_pdf_content(drive_url)
-if text:
-    st.write("Document extracted successfully!")
-
-    st.write("Creating vector store...")
-    vector_store, sentences = create_vector_store(text)
-
-    st.write("Vector store created successfully!")
-
-    query = st.text_input("Enter your query:")
-    if query:
-        st.write("Retrieving relevant context from the document...")
-        retriever = vector_store.as_retriever()
-        retriever.search_kwargs["k"] = 3  # Retrieve top 3 matches
-
-        # Define a prompt template to guide LLM response generation
-        prompt_template = PromptTemplate(
-            template="""
-            Use the following context to answer the question:
-            
-            {context}
-            
-            Question: {question}
-            Answer:""",
-            input_variables=["context", "question"]
-        )
-
-        # Create a RetrievalQA chain
-        qa_chain = RetrievalQA(
-            retriever=retriever,
-            llm=llm,
-            prompt=prompt_template
-        )
-
-        # Run the query through the QA chain
-        result = qa_chain.run(query)
-        st.write("Answer:", result)
-else:
-    st.error("Failed to extract content from the document.")