CHANGED ORIGINAL APP.PY TO APPeithoutResetButton.py and added new app.py

Try to fix : TypeError: gradio_chatbot() takes 1 positional argument but 2 were given

APPwithoutResetButton.py

@@ -0,0 +1,36 @@
+# # app.py
+
+# import gradio as gr
+# from pipeline import chatbot_response, generate_coherent_response, ChatHistory
+
+# # Create a new instance of chat history
+# chat_history = ChatHistory()
+
+# # Step 6: Define Gradio Interface for Chatbot
+# def gradio_chatbot(user_query):
+#     # Step 7: Retrieve relevant data for the query
+#     retrieved_data = chatbot_response(user_query)
+
+#     # Step 8: Check and verify the query for health/wellness content
+#     is_valid = True
+#     if is_valid:
+#         # Generate a coherent response using Groq's DeepSeek-R1 LLM
+#         coherent_response = generate_coherent_response(user_query, retrieved_data, chat_history.get_history())
+#     else:
+#         coherent_response = "The query does not seem to match health and wellness topics."
+
+#     # Add the user message and assistant response to the chat history
+#     chat_history.add_message("user", user_query)
+#     chat_history.add_message("assistant", coherent_response)
+
+#     # Return the response and the chat history
+#     return coherent_response, chat_history.get_history()
+
+# # Create the Gradio interface
+# iface = gr.Interface(fn=gradio_chatbot,
+#                      inputs=gr.Textbox(label="Ask a question"),
+#                      outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Chat History")],
+#                      title="Wellness Chatbot")
+
+# # Launch the Gradio interface
+# iface.launch()

app.py

@@ -1,5 +1,3 @@
-# app.py
-
 import gradio as gr
 from pipeline import chatbot_response, generate_coherent_response, ChatHistory
 
@@ -7,24 +5,23 @@ from pipeline import chatbot_response, generate_coherent_response, ChatHistory
 chat_history = ChatHistory()
 
 # Step 6: Define Gradio Interface for Chatbot
-def gradio_chatbot(user_query):
+def gradio_chatbot(user_query, history):
+    # Process query only if it's not empty
+    if not user_query.strip():
+        return "", history if history else ""
+        
     # Step 7: Retrieve relevant data for the query
     retrieved_data = chatbot_response(user_query)
 
     # Step 8: Check and verify the query for health/wellness content
     is_valid = True
     if is_valid:
-        # Generate a coherent response using Groq's DeepSeek-R1 LLM
-        coherent_response = generate_coherent_response(user_query, retrieved_data, chat_history.get_history())
+        # Generate a coherent response using Groq's model
+        coherent_response = generate_coherent_response(user_query, retrieved_data, history)
     else:
         coherent_response = "The query does not seem to match health and wellness topics."
 
-    # OLDER VERSION
-    # # Add the user message and assistant response to the chat history
-    # chat_history.add_message("user", user_query)
-    # chat_history.add_message("assistant", coherent_response)
-    # # Return the response and the chat history
-    # return coherent_response, chat_history.get_history()
+    # Add the messages to history
     if not history:
         history = ""
     if history:
@@ -32,8 +29,8 @@ def gradio_chatbot(user_query):
     history += f"User: {user_query}\nAssistant: {coherent_response}"
 
     return coherent_response, history
-    
 
+# Reset function to clear chat history and response
 def reset_chat():
     """Reset the chat interface."""
     chat_history.reset()
@@ -72,14 +69,4 @@ with gr.Blocks(title="Wellness Chatbot") as iface:
     )
 
 # Launch the Gradio interface
-iface.launch()
-
-#OLDER VERSION
-# # Create the Gradio interface
-# iface = gr.Interface(fn=gradio_chatbot,
-#                      inputs=gr.Textbox(label="Ask a question"),
-#                      outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Chat History")],
-#                      title="Wellness Chatbot")
-
-# # Launch the Gradio interface
-# iface.launch()
+iface.launch(share=True)