Deploying to HF

app.py

@@ -115,13 +115,7 @@ def create_model_from_checkpoint():
 model = create_model_from_checkpoint()
 labels = [ "benign", "malignant", "normal" ]
 
-def predict(gr_inputs):
-    img = gr_inputs[0]
-    shap_samples = gr_inputs[1]
-    shap_stdevs = gr_inputs[2]
-    occlusion_stride = gr_inputs[3]
-    occlusion_window = gr_inputs[4]
-    
+def predict(img, shap_samples, shap_stdevs, occlusion_stride, occlusion_window):
     explainer = Explainer(model, img, labels)
     return [explainer.confidences,
             explainer.shap(shap_samples, shap_stdevs),
@@ -137,5 +131,10 @@ ui = gr.Interface(fn=predict,
                     gr.Slider(minimum=4, maximum=80, default=15, label="Occlusion Window", step=1)
                 ],
                 outputs=[gr.Label(num_top_classes=3), gr.Image(type="pil"), gr.Image(type="pil"), gr.Image(type="pil")],
-                examples=["benign (52).png", "benign (243).png", "malignant (127).png", "malignant (201).png", "normal (81).png", "normal (101).png"]).launch()
+                examples=[["benign (52).png", 50, 0.0001, 8, 15],
+                          ["benign (243).png", 50, 0.0001, 8, 15],
+                          ["malignant (127).png", 50, 0.0001, 8, 15],
+                          ["malignant (201).png", 50, 0.0001, 8, 15],
+                          ["normal (81).png", 50, 0.0001, 8, 15], 
+                          ["normal (101).png", 50, 0.0001, 8, 15]]).launch()
 ui.launch(share=True)