Update app.py

app.py

@@ -1,47 +1,46 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+# Cell 1: Image Classification Model
 import gradio as gr
-import torch
+from transformers import pipeline
 
+image_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-title = "????AI ChatBot"
-description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
-examples = [["How are you?"]]
+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(
+    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?",
+)
 
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-
+# Cell 2: Chatbot Model
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(
-        input + tokenizer.eos_token, return_tensors="pt"
-    )
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-    # append the new user input tokens to the chat history
+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)
-
-    # generate a response
-    history = model.generate(
-        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    # print('decoded_response-->>'+str(response))
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    # print('response-->>'+str(response))
-    return response, history
-
-
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
-).launch()
+    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.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").style(container=False)
+    txt.submit(predict_chatbot, [txt, state], [chatbot, state])
+
+# Launch the interfaces
+if __name__ == "__main__":
+    image_gradio_app.launch()
+    chatbot_gradio_app.launch()