app.py

import gradio as gr
import requests
import random
from src.classification_model import ClassificationModel

#only for dummy data
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

clf = ClassificationModel()
model_names = clf.get_model_names()
output_labels = []

def predict(models, img_urls, img_files):
    print(f'model choosen: {models}')
    model_predictions = {}

    #set all labels visibility to false
    for i, name in enumerate(model_names):
        model_predictions[output_labels[i]] = gr.Label(label=f'# {name}', visible=False)
        print(f'id {i} invisible')
    
    for m in models:
        idx = model_names.index(m)
        print(f' {m} idx: ', idx)
        result = {labels[random.randrange(0, len(labels))]: random.uniform(0, 1.0) for i in range(5)}        
        model_predictions[output_labels[idx]] =  gr.Label(label=f'# {m}, 3 seconds', value=result, visible=True)
    
    return model_predictions

with gr.Blocks() as demo:
    gr.Markdown("# Image Classification Benchmark")
    
    with gr.Row():
        with gr.Column(scale=1):
            model = gr.Dropdown(choices=model_names, multiselect=True, label='Choose the model')
            img_urls = gr.Textbox(label='Image Urls (separated with comma)')    
            img_files = gr.File(label='Upload Files',file_count='multiple', file_types=['image'])
            apply = gr.Button("Classify", variant='primary')
        with gr.Column(scale=1):
            for name in clf.get_model_names():
                output_labels.append(gr.Label(label=f'# {name}'))                       

    apply.click(fn=predict,
               inputs=[model, img_urls, img_files],
               outputs=output_labels)


if __name__ == "__main__":
    demo.launch()

# inputs = [
#     gr.Dropdown(choices=clf.get_model_names(), multiselect=True)
#     ]

# iface = gr.Interface(fn=greet, inputs=inputs, outputs="text")
# iface.launch()