Spaces:
Runtime error
Runtime error
File size: 1,131 Bytes
53d6474 eb09c16 cad1126 87bd002 1674572 acf5da7 87bd002 6f8418a dfef0b8 87bd002 6f8418a eb09c16 6f8418a acf5da7 2471c01 6f8418a 87bd002 1674572 6f8418a 87bd002 89934b5 6f8418a f718f04 89934b5 f718f04 2471c01 1674572 f718f04 89934b5 0930360 f718f04 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
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="guillen/vit-basura-test1")
def predict_image(input_img):
predictions = image_pipeline(input_img)
return {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.Label(label="Result")],
title="Green Greta",
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() |