Update app.py

app.py

@@ -72,51 +72,77 @@ def query_pdf(file,user_query,openai_api_key):
     response = qa_chain.run(user_query)
     return response
 
-    # Define Gradio interface for the summarization
-    def create_gradio_interface():
-        with gr.Blocks() as demo:
-            gr.Markdown("### ChatPDF and Research Paper Summarizer using GPT-4 and Langchain ")
+# Function to handle user queries and provide answers from the document
+def query_pdf(file, user_query, openai_api_key):
+    # Set the OpenAI API key dynamically
+    openai.api_key = openai_api_key
 
-            # Input field for API key
-            with gr.Row():
-                openai_api_key_input=gr.Textbox(label="Enter OpenAI API key",type ="password",placeholder="Enter your openai api key here")
+    # Load and process the PDF
+    documents = load_pdf(file)
 
-                with gr.Tab("Summarize PDF"):
-                    with gr.Row():
-                        pdf_file = gr.file(label="Upload PDF Document")
-                        summarize_btn=gr.Button("Summarize")
-                        summary_output=gr.Textbox(label="Summary",interactive=False)
-                        clear_btn_summary=gr.Button("Clear Response")
+    # Create embeddings for the documents
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
 
+    # Use LangChain's FAISS Vector Store to store and search the embeddings
+    vector_store = FAISS.from_documents(documents, embeddings)
 
-                        #Summarize Button Logic
-                        summarize_btn.click(summarize_pdf,inputs=[pdf_file,openai_api_key_input],outputs=summary_output)
+    # Create a RetrievalQA chain for querying the document
+    llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_api_key)  # Passing API key here
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vector_store.as_retriever()
+    )
 
-                        # Clear response Button Logic for summary Tab
-                        clear_btn_summary.click(lambda:"",inputs=[],outputs=summary_output)
+    # Query the model for the user query
+    response = qa_chain.run(user_query)
+    return response
 
-                with gr.Tab("Ask Questions"):
-                    with gr.Row():
-                        pdf_file_q = gr.File(label="Upload PDF Document")
-                        user_input = gr.Textbox(label="Enter your question")
-                        answer_output = gr.Textbox(label="Answer",interactive=False)
-                        clear_btn_answer = gr.Button("clear Response")
+# Define Gradio interface for the summarization
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("### ChatPDF and Research Paper Summarizer using GPT-4 and LangChain")
+        
+        # Input field for API Key
+        with gr.Row():
+            openai_api_key_input = gr.Textbox(label="Enter OpenAI API Key", type="password", placeholder="Enter your OpenAI API key here")
 
-                    # Submit Question Logic
-                    user_input.submit(query_pdf,inputs=[pdf_file_q,user_input,openai_api_key_input],outputs=answer_output)
+        with gr.Tab("Summarize PDF"):
+            with gr.Row():
+                pdf_file = gr.File(label="Upload PDF Document")
+                summarize_btn = gr.Button("Summarize")
+                summary_output = gr.Textbox(label="Summary", interactive=False)
+                clear_btn_summary = gr.Button("Clear Response")
+
+            # Summarize Button Logic
+            summarize_btn.click(summarize_pdf, inputs=[pdf_file, openai_api_key_input], outputs=summary_output)
+
+            # Clear Response Button Logic for Summary Tab
+            clear_btn_summary.click(lambda: "", inputs=[], outputs=summary_output)
+        
+        with gr.Tab("Ask Questions"):
+            with gr.Row():
+                pdf_file_q = gr.File(label="Upload PDF Document")
+                user_input = gr.Textbox(label="Enter your question")
+                answer_output = gr.Textbox(label="Answer", interactive=False)
+                clear_btn_answer = gr.Button("Clear Response")
 
+            # Submit Question Logic
+            user_input.submit(query_pdf, inputs=[pdf_file_q, user_input, openai_api_key_input], outputs=answer_output)
 
-                    # Clear response button logic for anser tab
-                    clear_btn_answer.click(lambda:"",inputs=[],outputs=answer_output)
+            # Clear Response Button Logic for Answer Tab
+            clear_btn_answer.click(lambda: "", inputs=[], outputs=answer_output)
 
-                    user_input.submit(None,None,answer_output)
+            user_input.submit(None, None, answer_output)  # Clear answer when typing new query
+    
     return demo
 
 # Run Gradio app
-if __name__=="__main__":
+if __name__ == "__main__":
     demo = create_gradio_interface()
     demo.launch(debug=True)
 
+