Create app.py

app.py

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+import gradio as gr
+import torch
+import onnxruntime as ort
+
+# Initialize the ONNX session
+session = ort.InferenceSession("/content/bone_age_model.onnx")
+
+# Define the inference function
+def inference(sample_name):
+    sample = torch.load(f"/content/samples/{sample_name}")
+    age = sample['boneage'].item()
+    outputs = session.run(None, {"input": sample['path'].numpy()})
+    predicted_age = (outputs[0]*41.172)+127.329
+    return {
+        'Bone age': age,
+        'Predicted Bone age': predicted_age[0][0]
+    }
+
+# List of sample file names
+sample_files = [f"sample_{i}.pth" for i in range(1, 11)]
+
+# Create Gradio interface
+dropdown = gr.inputs.Dropdown(choices=sample_files, label="Select a sample")
+
+iface = gr.Interface(
+    fn=inference,
+    inputs=dropdown,
+    outputs=[gr.outputs.Textbox(label="Bone Age"), gr.outputs.Textbox(label="Predicted Bone Age")],
+    title="Bone Age Prediction",
+    description="Select a sample from the dropdown to see the bone age and predicted bone age."
+)
+
+# Launch the app
+iface.launch()