Create app.py

app.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+
+# Classes must match your training dataset
+class_names = [ "A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z" ]
+
+# Transform (same as training)
+transform = transforms.Compose([
+    transforms.Resize((224,224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5]*3, [0.5]*3)
+])
+
+# Load model
+def load_model():
+    model = models.mobilenet_v2(pretrained=False)
+    model.classifier[1] = nn.Linear(model.classifier[1].in_features, len(class_names))
+    model.load_state_dict(torch.load("isl_model.pth", map_location="cpu"))
+    model.eval()
+    return model
+
+model = load_model()
+
+# Prediction function
+def predict(img: Image.Image):
+    with torch.no_grad():
+        x = transform(img).unsqueeze(0)
+        out = model(x)
+        return class_names[out.argmax(1).item()]
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="ISL Alphabet Recognition",
+    description="Upload a hand sign image (A–Z) to get the predicted letter."
+)
+
+demo.launch()