app.py

import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import torch
import theme

theme = theme.Theme()

# 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?",
    theme=theme
)

# Cell 2: Chatbot Model
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")

def echo(message, history):
    return message

chatbot_gradio_app = gr.ChatInterface(
    fn=echo,
    title="Greta",
    theme=theme
)

# Combine both interfaces into a single app
gr.TabbedInterface(
    [image_gradio_app, chatbot_gradio_app],
    tab_names=["Greta Image","Greta Chat"],
    theme=theme
).launch()