Update app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Cell 1: Image Classification Model
+image_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+def predict_image(input_img):
+    predictions = image_pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
+
+image_gradio_app = gr.Interface(
+    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
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+def predict_chatbot(input, history=[]):
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+    history = chatbot_model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+    response = tokenizer.decode(history[0]).split("")
+
+    response_tuples = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]
+    return response_tuples, history
+
+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()