app.py

import os
import gradio as gr
from huggingface_hub import login
from huggingface_hub import snapshot_download
import numpy as np
import tensorflow as tf
import cv2

IMG_WIDTH = 32
IMG_HEIGHT = 32

classes = { 0:'Speed limit (20km/h)',
            1:'Speed limit (30km/h)', 
            2:'Speed limit (50km/h)', 
            3:'Speed limit (60km/h)', 
            4:'Speed limit (70km/h)', 
            5:'Speed limit (80km/h)', 
            6:'End of speed limit (80km/h)', 
            7:'Speed limit (100km/h)', 
            8:'Speed limit (120km/h)', 
            9:'No passing', 
            10:'No passing veh over 3.5 tons', 
            11:'Right-of-way at intersection', 
            12:'Priority road', 
            13:'Yield', 
            14:'Stop', 
            15:'No vehicles', 
            16:'Veh > 3.5 tons prohibited', 
            17:'No entry', 
            18:'General caution', 
            19:'Dangerous curve left', 
            20:'Dangerous curve right', 
            21:'Double curve', 
            22:'Bumpy road', 
            23:'Slippery road', 
            24:'Road narrows on the right', 
            25:'Road work', 
            26:'Traffic signals', 
            27:'Pedestrians', 
            28:'Children crossing', 
            29:'Bicycles crossing', 
            30:'Beware of ice/snow',
            31:'Wild animals crossing', 
            32:'End speed + passing limits', 
            33:'Turn right ahead', 
            34:'Turn left ahead', 
            35:'Ahead only', 
            36:'Go straight or right', 
            37:'Go straight or left', 
            38:'Keep right', 
            39:'Keep left', 
            40:'Roundabout mandatory', 
            41:'End of no passing', 
            42:'End no passing veh > 3.5 tons' }

def image_mod(image):
    
    # Resize image to the dimensions used when training 
    size = IMG_WIDTH, IMG_HEIGHT
    res = cv2.resize(image, size, interpolation=cv2.INTER_AREA)

    # Convert image from PIL format (RGB) to the cv2 format
    # (BGR) that was used when training the model
    res = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)

    # Convert to float and normalize to match the training
    res = res.astype("float32") / 255.0
    
    # Convert single image to a batch for prediction
    res = np.array([res])  
    
    # Carry out prediction
    result = model.predict(res)

    # Convert to format suitable for the Label Gradio component
    confidences = result[0]
    return {F"{index}: {classes[index]}":element for index, element in enumerate(confidences)}

# Download the model from Hugging Face Hub
login(token=os.environ['TOKEN_TRAFFIC'])
model_path = snapshot_download(repo_id=os.environ['REPO_TRAFFIC_MODEL'])
model = tf.keras.models.load_model(model_path)

# Configure Gradio components
input_image_component = gr.Image(type="numpy")
output_label_component = gr.Label(num_top_classes=5)

# Configure user interface
iface = gr.Interface(fn=image_mod, inputs=input_image_component, outputs=output_label_component, live=True, title="German traffic sign recognizer", description="A convolutional neural network to categorize images of German traffic signs.", article="# Reference\nJ. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460. 2011.", examples="examples")

# Launch the frontend server
iface.launch(share=False)