Update app.py

app.py

@@ -1,7 +1,9 @@
-# Cell 1: Image Classification Model
+# Combined Interface
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import torch
 
+# Image Classification Model
 image_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
 def predict_image(input_img):
@@ -9,17 +11,13 @@ def predict_image(input_img):
     return input_img, {p["label"]: p["score"] for p in predictions} 
 
 image_gradio_app = gr.Interface(
-    predict_image,
+    fn=predict_image,
     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
     title="Hot Dog? Or Not?",
 )
 
-# Cell 2: Chatbot Model
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
+# Chatbot Model
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
@@ -32,15 +30,15 @@ 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 the interfaces
-if __name__ == "__main__":
-    image_gradio_app.launch()
-    chatbot_gradio_app.launch()
+chatbot_gradio_app = gr.Interface(
+    fn=predict_chatbot,
+    inputs=[gr.Textbox(show_label=False, placeholder="Enter text and press enter"), gr.State()],
+    outputs=[gr.Chatbot(), gr.State()],
+    live=True
+)
+
+# Display both interfaces vertically
+gr.Interface(
+    columns=2,
+    children=[image_gradio_app, chatbot_gradio_app]
+).launch()