Spaces:
Running
Running
File size: 1,176 Bytes
cf49c4b 801ca00 cf49c4b 801ca00 a1ff6d2 cf49c4b 5d7c9cd cf49c4b 801ca00 313d70a 2997b62 313d70a c3f5319 cf49c4b b3fa9dd e02c7a0 313d70a c3f5319 313d70a b3fa9dd c3f5319 e02c7a0 |
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 torch
import os
import random
import gradio as gr
from transformers import pipeline
import base64
from datasets import load_dataset
from diffusers import DiffusionPipeline
from huggingface_hub import login
import numpy as np
def guessanImage(model, image):
imgclassifier = pipeline("image-classification", model=model)
if image is not None:
description = imgclassifier(image)
return description
def guessanAge(model, picture):
imgclassifier = pipeline("image-classification", model=model)
if image is not None:
description = imgclassifier(image)
return description
radio1 = gr.Radio(["microsoft/resnet-50", "google/vit-base-patch16-224", "apple/mobilevit-small"], label="Select a Classifier", info="Image Classifier")
tab1 = gr.Interface(
fn=guessanImage,
inputs=[radio1, gr.Image(type="pil")],
outputs=["text"],
)
radio2 = gr.Radio(["nateraw/vit-age-classifier"], label="Select an Age Classifier", info="Age Classifier")
tab2 = gr.Interface(
fn=guessanAge,
inputs=[radio2, gr.Image(type="pil")],
outputs=["text"],
)
demo = gr.TabbedInterface([tab1, tab2], ["tab1", "tab2"])
demo.launch()
|