first commit

app.py

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+from pathlib import Path
+from PIL import Image
+import numpy as np
+import torch
+import requests
+from io import BytesIO
+from torchvision.models import resnet18, ResNet18_Weights
+
+def predict(img_path = None) -> str:
+    # Initialize the model and transform
+    resnet_model = resnet18(weights=ResNet18_Weights.DEFAULT)
+    resnet_transform = ResNet18_Weights.DEFAULT.transforms()
+    
+    
+    # Load the image
+    if img_path is None:
+        image = Image.open("examples/steak.jpeg").convert("RGB")
+        
+    if isinstance(img_path, np.ndarray):
+            img = Image.fromarray(img_path.astype("uint8"), "RGB")
+
+            # img = effnet_b2_transform(img).unsqueeze(0)
+        
+        
+        
+
+    # Convert to tensor
+    # img = torch.from_numpy(np.array(image)).permute(2, 0, 1)
+    img = resnet_transform(img)
+
+    # Inference
+    resnet_model.eval()
+    with torch.inference_mode():
+        logits = resnet_model(img.unsqueeze(0))
+        pred_class = torch.softmax(logits, dim=1).argmax(dim=1).item()
+        predicted_label = ResNet18_Weights.DEFAULT.meta["categories"][pred_class]
+        print(f"Predicted class: {predicted_label}")
+        return predicted_label
+
+
+import numpy as np
+import gradio as gr
+
+
+demo = gr.Interface(predict, gr.Image(), "label")
+if __name__ == "__main__":
+    demo.launch()
+
+