Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Image Classification Model
+# Cell 1: Image Classification Model
 image_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
 def predict_image(input_img):
@@ -16,7 +16,7 @@ image_gradio_app = gr.Interface(
     title="Hot Dog? Or Not?",
 )
 
-# Chatbot Model
+# Cell 2: Chatbot Model
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
@@ -29,17 +29,17 @@ def predict_chatbot(input, history=[]):
     response_tuples = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]
     return response_tuples, history
 
-chatbot_gradio_app = gr.Blocks()
-with chatbot_gradio_app as demo:
-    chatbot = gr.Chatbot()
-    state = gr.State([])
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter")
-    txt.submit(predict_chatbot, [txt, state], [chatbot, state])
-
-# Launch both interfaces
-if __name__ == "__main__":
-    gr.Interface(
-        columns=2,
-        children=[image_gradio_app, chatbot_gradio_app]
-    ).launch()
+chatbot_gradio_app = gr.Interface(
+    fn=predict_chatbot,
+    inputs=gr.Textbox(show_label=False, placeholder="Enter text and press enter"),
+    outputs=gr.Textbox(),
+    live=True,
+    title="Chatbot",
+)
+
+
+# Combine both interfaces into a single app
+gr.TabbedInterface(
+    [image_gradio_app, chatbot_gradio_app],
+    tab_names=["image","chatbot"]
+).launch()