add application files

app.py

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+import torch
+import torchvision.models as models
+from PIL import Image
+from efficientnet_pytorch import EfficientNet
+import torchvision.transforms as transforms
+import gradio as gr
+from gradio import components
+import numpy as np
+
+def predict(image):
+    image = Image.fromarray(np.uint8(image)).convert('RGB')
+
+    model = EfficientNet.from_name('efficientnet-b7', num_classes=2)
+    model_weights_path = 'efficientnetb7_tyrequality_classifier.pth'
+    model.load_state_dict(torch.load(model_weights_path))
+    model.eval()
+
+    transform = transforms.Compose([
+        transforms.Resize(224),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406],
+                            [0.229, 0.224, 0.225])
+    ])
+
+    input_data = transform(image).unsqueeze(0)
+
+    class_to_label = {0: 'defective', 1: 'good'}
+
+    with torch.no_grad():
+        output = model(input_data)
+
+    # Get the predicted class label
+    _, predicted_class = torch.max(output, 1)
+
+    probs = torch.nn.functional.softmax(output, dim=1)
+    print(probs, "probs")
+    conf, _ = torch.max(probs, 1)
+
+    result = "Tire status is {} with confidence level in {}%".format(class_to_label[predicted_class.item()], conf.item()*100)
+    return result
+
+iface = gr.Interface(fn=predict,
+                     inputs=gr.Image(),
+                     outputs="textbox")
+
+iface.launch(share=True)

requirements.txt

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+torch 
+torchvision
+efficientnet_pytorch 
+gradio 
+numpy