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.ipynb_checkpoints/main-checkpoint.py

@@ -0,0 +1,18 @@
+import gradio as gr
+from transformers import pipeline
+
+pipeline = pipeline("image-classification", model="jhoppanne/SkinCancerClassifier_Plain-V0")
+
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return predictions
+
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Input Skin Image", sources=['upload', 'webcam'], type="pil"),
+    outputs="text",
+    title="How severe is my Skin Cancer?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()

main.py

@@ -5,12 +5,12 @@ pipeline = pipeline("image-classification", model="jhoppanne/SkinCancerClassifie
 
 def predict(input_img):
     predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+    return predictions
 
 gradio_app = gr.Interface(
     predict,
     inputs=gr.Image(label="Input Skin Image", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    outputs="text",
     title="How severe is my Skin Cancer?",
 )