Update app.py

app.py

@@ -51,12 +51,12 @@ def ask_openai_gpt4(question):
     return response.choices[0].message.content
 
 def chatbot(user_input):
-    log_output = StringIO()  # To capture logs
+    log_output = StringIO() 
 
     faiss_index, question_embeddings = load_embeddings_and_faiss()
     embedding_model = OpenAIEmbeddings(openai_api_key=openai.api_key)
     
-    start_time = time.time()  # Start timer
+    start_time = time.time()
     
     log_output.write("Retrieving answer from FAISS...\n")
     response_text = retrieve_answer(user_input, faiss_index, embedding_model, answers, log_output, threshold=0.3)
@@ -65,29 +65,24 @@ def chatbot(user_input):
         log_output.write("No good match found in dataset. Using GPT-4o-mini to generate an answer.\n")
         response_text = ask_openai_gpt4(user_input)
 
-    end_time = time.time()  # End timer
-    response_time = end_time - start_time  # Calculate response time
+    end_time = time.time() 
+    response_time = end_time - start_time  
 
-    # Log the final response time
-
-    # Return the chatbot response, response time, and log
     return response_text, f"Response time: {response_time:.4f} seconds", log_output.getvalue()
 
-# Simplified Gradio interface with response, response time, and logs
 demo = gr.Interface(
-    fn=chatbot,  # Main chatbot function
-    inputs="text",  # User input: single text field
+    fn=chatbot,  
+    inputs="text", 
     outputs=[
-        gr.Textbox(label="Chatbot Response"),   # Named output for the chatbot response
-        gr.Textbox(label="Response Time"),      # Named output for the response time
-        gr.Textbox(label="Logs")  # Logs
+        gr.Textbox(label="Chatbot Response"),
+        gr.Textbox(label="Response Time"),   
+        gr.Textbox(label="Logs") 
     ],
     title="Medical Chatbot with Custom Knowledge About Medical FAQ",
     description="A chatbot with custom knowledge using FAISS for quick responses or fallback to GPT-4o-mini when no relevant answer is found. Response time is also tracked."
 )
 
 if __name__ == "__main__":
-    # Load dataset
     df = pd.read_csv("medquad.csv")
     questions = df['question'].tolist()
     answers = df['answer'].tolist()