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app.py

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+import gradio as gr
+from fastai.vision.widgets import *
+from fastcore.all import *
+from fastai.vision.all import *
+
+def classify_image(img):
+    learn = load_learner('nihal_model.pkl')
+    pred,pred_idx,probs = learn.predict(img)
+    #return both the prediction and the probability either nihal or not and format in perccentage
+    return pred, "{:.0%}".format(float(probs[pred_idx])),float(probs[~pred_idx])
+    #return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+
+
+if __name__ == '__main__':
+    # Define inputs and outputs for Gradio interface
+    inputs = [gr.Image(type="pil", label="Select an image of Nihal")]
+    outputs = [
+        gr.Label(""),
+        gr.Label(label="Confidence"),
+    ]
+
+    # Launch the Gradio interface
+    interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs)
+    interface.launch()

nihal_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4fc562781d4d8b35f72802117e0f1b50ed654f87e4349cf0d54db606e88a3cdf
+size 87480610