Update app.py

app.py

@@ -108,85 +108,5 @@ with gr.Blocks(title="Chat with PDF") as demo:
     process_button.click(process_pdfs, inputs=[pdf_input], outputs=[status_output])
     ask_button.click(query_pdf, inputs=[question_input], outputs=[answer_output])
 
-if __name__ == "__main__":
-    demo.launch()    try:
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
-        vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-        vector_store.save_local(INDEX_PATH)
-        return "PDFs processed successfully! Vector store saved. Now you can ask questions."
-    except Exception as e:
-        return f"Error creating vector store: {str(e)}"
-
-def load_vector_store():
-    try:
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
-        if os.path.exists(INDEX_PATH):
-            return FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
-        return None
-    except Exception as e:
-        return None
-
-def get_qa_chain():
-    # Modern stuff QA chain
-    llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.3, google_api_key=GOOGLE_API_KEY)
-
-    qa_prompt = ChatPromptTemplate.from_messages([
-        ("system", """
-        Answer the question as detailed as possible from the provided context only.
-        If the answer is not in the provided context, respond with "answer is not available in the context".
-        Do not make up information.
-
-        Context: {context}
-        """),
-        ("human", "{input}"),
-    ])
-
-    stuff_chain = create_stuff_documents_chain(llm, qa_prompt)
-    return stuff_chain
-
-def query_pdf(user_question):
-    vector_store = load_vector_store()
-    if vector_store is None:
-        return "Please process a PDF first by uploading and submitting it."
-
-    try:
-        retriever = vector_store.as_retriever(search_kwargs={"k": 4})  # Retrieve top 4 docs
-        stuff_chain = get_qa_chain()
-
-        # Full retrieval QA chain
-        retrieval_chain = create_retrieval_chain(retriever, stuff_chain)
-
-        response = retrieval_chain.invoke({"input": user_question})
-        return response["answer"]
-    except Exception as e:
-        return f"Error querying the PDF: {str(e)}"
-
-def process_pdfs(pdf_files):
-    if not pdf_files:
-        return "Please upload at least one PDF."
-
-    raw_text = get_pdf_text(pdf_files)
-    if "Error" in raw_text:
-        return raw_text
-    if not raw_text.strip():
-        return "No extractable text found in the uploaded PDFs."
-
-    text_chunks = get_text_chunks(raw_text)
-    result = create_vector_store(text_chunks)
-    return result
-
-# Gradio UI
-with gr.Blocks(title="Chat with PDF") as demo:
-    gr.Markdown("## Chat with PDF 💁")
-    pdf_input = gr.File(file_types=[".pdf"], label="Upload PDF(s)", file_count="multiple")
-    process_button = gr.Button("Submit & Process")
-    status_output = gr.Textbox(label="Status", placeholder="Status updates will appear here...")
-    question_input = gr.Textbox(label="Ask a Question from the PDF")
-    answer_output = gr.Textbox(label="Reply", placeholder="Answers will appear here...")
-    ask_button = gr.Button("Get Answer")
-
-    process_button.click(process_pdfs, inputs=[pdf_input], outputs=[status_output])
-    ask_button.click(query_pdf, inputs=[question_input], outputs=[answer_output])
-
 if __name__ == "__main__":
     demo.launch()