app.py

import gradio as gr

import torch
import torch.nn.functional as F

from torchvision import transforms 

def transform_img(img):
    # Transformations that will be applied
    the_transform = transforms.Compose([
            transforms.Resize((224,224)),
            transforms.CenterCrop((224,224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
    ])
    return the_transform(img)

    # Returns string with class and probability
def classify_img(img):
    class_names = ['AIR COMPRESSOR',
 'ALTERNATOR',
 'BATTERY',
 'BRAKE CALIPER',
 'BRAKE PAD',
 'BRAKE ROTOR',
 'CAMSHAFT',
 'CARBERATOR',
 'CLUTCH PLATE',
 'COIL SPRING',
 'CRANKSHAFT',
 'CYLINDER HEAD',
 'DISTRIBUTOR',
 'ENGINE BLOCK',
 'ENGINE VALVE',
 'FUEL INJECTOR',
 'FUSE BOX',
 'GAS CAP',
 'HEADLIGHTS',
 'IDLER ARM',
 'IGNITION COIL',
 'INSTRUMENT CLUSTER',
 'LEAF SPRING',
 'LOWER CONTROL ARM',
 'MUFFLER',
 'OIL FILTER',
 'OIL PAN',
 'OIL PRESSURE SENSOR',
 'OVERFLOW TANK',
 'OXYGEN SENSOR',
 'PISTON',
 'PRESSURE PLATE',
 'RADIATOR',
 'RADIATOR FAN',
 'RADIATOR HOSE',
 'RADIO',
 'RIM',
 'SHIFT KNOB',
 'SIDE MIRROR',
 'SPARK PLUG',
 'SPOILER',
 'STARTER',
 'TAILLIGHTS',
 'THERMOSTAT',
 'TORQUE CONVERTER',
 'TRANSMISSION',
 'VACUUM BRAKE BOOSTER',
 'VALVE LIFTER',
 'WATER PUMP',
 'WINDOW REGULATOR']
    model = torch.jit.load("car_part_traced_classifier_resnet50.ptl")
    # Applying transformation to the image
    model_img = transform_img(img)
    model_img = model_img.view(1,3,224,224)
    
    # Running image through the model
    model.eval()
    with torch.no_grad():
        result = model(model_img)
    
    # Converting values to softmax values 
    result = F.softmax(result,dim=1)
    # Grabbing top 3 indices and probabilities for each index
    top3_prob, top3_catid = torch.topk(result,3)
    
    # Dictionary I will display
    model_output = {}
    for i in range(top3_prob.size(1)):
        
        model_output[class_names[top3_catid[0][i].item()]] = top3_prob[0][i].item()
    print(model_output)
    return model_output 

demo = gr.Interface(classify_img, gr.Image(type='pil'), outputs = gr.Label(num_top_classes=3))
demo.launch()