Create app.py

app.py

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+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+
+model_path = hf_hub_download(repo_id="Ayamohamed/DiaClassification", filename="dia_none_classifier_full.pth")
+model = torch.load(model_path, map_location=torch.device("cpu"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+def predict(image):
+    image = transform(image).unsqueeze(0)  
+    with torch.no_grad():
+        output = local_model(image)
+        probabilities = F.softmax(output, dim=1)
+        class_idx = torch.argmax(probabilities, dim=1).item()
+        return "Diagram" if class_idx == 0 else "Not Diagram"
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Diagram Classifier"
+).launch(share=True)