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import gradio as gr
from transformers import pipeline
import tensorflow
import torch
global pipeline
global default_model_name
default_model_name = "google/vit-base-patch16-224"
def predict(image, model_name):
if model_name == "":
model_name = default_model_name
pipe = pipeline(task="image-classification", model=model_name)
predictions = pipe(image)
return {p["label"]: p["score"] for p in predictions}
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
"""
# Settings
[Here](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads) are some popular image classification models.
Or use default model **"google/vit-base-patch16-224"**
""")
gr.Markdown(
"""
# Image Classifier Result
""")
with gr.Row():
with gr.Column(scale=1):
#input_model = gr.Textbox(label="Enter a custom model name:", value=default_model_name, scale=1)
input_model = gr.Textbox(label="Enter a custom model name:", scale=1)
gr.Markdown("Upload image")
#images_input = gr.File(file_count="multiple", file_types=["image"], label="Input images", scale=1)
#images_input = gr.Files(file_count="multiple", file_types=["image"], label="Input images", scale=1)
input_image = gr.Image(label="Input Image", type="filepath")
#output = gr.Label(label="Output", num_top_classes=3, scale=2)
output = gr.Label(num_top_classes=10, scale=2)
with gr.Row(equal_height=True):
clear_button = gr.ClearButton(value="Clear", scale=0)
submit_button = gr.Button(value="Submit", variant="primary", scale=0)
submit_button.click(fn=predict, inputs=[input_image, input_model], outputs=output)
clear_button.click(lambda: [None, None, None], outputs=[input_model, input_image, output])
demo.launch()
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