Update app.py

app.py

@@ -7,25 +7,28 @@ hotdog_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-n
 # Load the chatbot model (DialoGPT)
 chatbot_pipeline = pipeline(task="text-generation", model="microsoft/DialoGPT-medium")
 
-def predict(input_data, option):
+def predict(input_data, option, chat_input=""):
     if option == "Image Classification":
         # Predict hotdog or not
         hotdog_predictions = hotdog_pipeline(input_data)
         return input_data, {
             "Hotdog Classification": {p["label"]: p["score"] for p in hotdog_predictions},
+            "Chatbot Response": "",  # Placeholder for chatbot response when not selected
         }
     elif option == "Chatbot":
         # Generate chatbot response
-        chatbot_response = chatbot_pipeline(input_data, max_length=50)[0]['generated_text']
+        chatbot_response = chatbot_pipeline(chat_input, max_length=50)[0]['generated_text']
         return input_data, {
-            "Chatbot Response": chatbot_response
+            "Hotdog Classification": {},
+            "Chatbot Response": chatbot_response,
         }
 
 iface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-        gr.Dropdown(label="Choose Option", choices=["Image Classification", "Chatbot"])
+        gr.Dropdown(label="Choose Option", choices=["Image Classification", "Chatbot"]),
+        gr.Textbox(label="Chatbot Input", placeholder="Type something to chat with the bot", type="text"),
     ],
     outputs=[
         gr.Image(label="Processed Image"),