app.py

import gradio as gr
import numpy as np
from PIL import Image
import os
import json

from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference

models_name = [
    "VGG16",
    "ShuffleNetV2",
    "mobilenet_v2"
]

colname = "mobilenet_v2"

radio = gr.inputs.Radio(models_name, default="mobilenet_v2", type="value", label=colname)
print(radio.label)

def predict_image(image, model_name):

    image = Image.fromarray(np.uint8(image)).convert('RGB')

    print("======================")
    print(type(image))
    print(type(model_name))
    print("==========")
    print(image)
    print(model_name)
    print("======================")

    # image = np.array(image) / 255
    # image = np.expand_dims(image, axis=0)

    classifier = TorchVisionClassifierInference(
        model_path = "./models/" + colname,
    )

    pred = classifier.predict_image(img=image)

    acc = dict((labels[i], 0.0) for i in range(len(labels)))
    acc[pred] = 100.0
    return acc
    # return pred

# open categories.txt in read mode
categories = open("categories.txt", "r")
labels = categories.readline().split(";")

image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
print(image)
label = gr.outputs.Label(num_top_classes=len(labels))

samples = ['./samples/basking.jpg', './samples/blacktip.jpg']
# , './samples/blacktip.jpg', './samples/blue.jpg', './samples/bull.jpg', './samples/hammerhead.jpg',
#         './samples/lemon.jpg', './samples/mako.jpg', './samples/nurse.jpg', './samples/sand tiger.jpg', './samples/thresher.jpg', 
#         './samples/tigre.jpg', './samples/whale.jpg', './samples/white.jpg', './samples/whitetip.jpg']
        
interface = gr.Interface(
    fn=predict_image, 
    inputs=[image, radio], 
    outputs=label, 
    capture_session=True, 
    allow_flagging=False, 
    # examples=samples
)
interface.launch()