initial version of sticker-defect app

app.py

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+import gradio as gr
+import torch
+import time
+from fastai.vision.all import load_learner
+from PIL import Image
+
+# Load the exported model
+model = load_learner("model.pkl")
+
+# Function to classify an image
+def classify_images(imgs):
+    start_time = time.time()
+    results = []
+    for img in imgs:
+        # Convert gradio image to PIL Image
+        pil_img = Image.fromarray(img.astype('uint8'), 'RGB')
+
+        # Perform inference
+        pred_class, pred_idx, pred_probs = model.predict(pil_img)
+
+        # Format output
+        output = f"Image Name: {img.name} - Category: {pred_class}"
+        results.append(output)
+
+    # Calculate total inference time
+    inference_time = time.time() - start_time
+
+    # Append total inference time to results
+    results.append(f"Total Inference Time: {inference_time:.2f} seconds")
+
+    return results
+
+# Create Gradio interface
+input_component = gr.inputs.Image(label="Upload Image", type="file", multiple_files=True)
+output_component = gr.outputs.Textbox(label="Classification Results")
+interface = gr.Interface(fn=classify_images, inputs=input_component, outputs=output_component, title="Image Classifier")
+
+# Launch the Gradio interface
+interface.launch()