app.py

import gradio as gr
from transformers import pipeline

pipeline_hotdog = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

chatbot_pipeline = pipeline(task="text-generation", model="microsoft/DialoGPT-medium")

def predict(input_img,chat_input):
    hotdog_predictions = pipeline_hotdog(input_img)
        # Generate chatbot response
    chatbot_response = chatbot_pipeline(chat_input, max_length=50)[0]['generated_text']

    return input_img, {
        "Hotdog Classification": {p["label"]: p["score"] for p in hotdog_predictions},
        "Chatbot Response": chatbot_response
    }
    
gradio_app = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
        gr.Textbox(label="Chatbot Input", placeholder="Type something to chat with the bot")
    ],
    outputs=[
        gr.Image(label="Processed Image"),
        gr.Label(label="Hotdog Classification", num_top_classes=2),
        gr.Textbox(label="Chatbot Response", output_transform=lambda x: x["Chatbot Response"], type="readonly")
    ],
    title="Hot Dog? Or Not? with Chatbot",
)

if _name_ == "_main_":
    gradio_app.launch()