test demo

app.py

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+import torch
+import gradio as gr
+from torchvision import models, transforms
+from PIL import Image
+import requests
+from huggingface_hub import hf_hub_download
+
+# Load the model checkpoint from Hugging Face
+checkpoint_path = hf_hub_download(repo_id="ttoosi/resnet50_robust_face", filename="100_checkpoint.pt")
+
+# Initialize the model
+model = models.resnet50()
+model.load_state_dict(torch.load(checkpoint_path))
+model.eval()
+
+# Image preprocessing
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Function to make predictions
+def predict(image):
+    image = preprocess(image).unsqueeze(0)  # Add batch dimension
+    with torch.no_grad():
+        output = model(image)
+    _, predicted_class = output.max(1)
+    return f"Predicted class: {predicted_class.item()}"
+
+# Create the Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="text")
+
+# Launch the interface
+iface.launch()