Create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the image classification pipeline
+pipeline = pipeline(task="image-classification", model="Hemg/Melanoma-Cancer-Image-Classification")
+
+def predict(input_img):
+    try:
+        # Use the pipeline to make predictions
+        predictions = pipeline(input_img)
+        # Process the predictions
+        result = {p["label"]: p["score"] for p in predictions}
+    except:
+        # If an exception occurs (e.g., out-of-context image), return a default result
+        result = {"out_of_context": 1.0}
+    
+    # Return the input image and the result
+    return input_img, result
+
+# Create a Gradio interface
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Melanoma-Cancer-Image-classification", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Benign ? Or Malignant?"
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    gradio_app.launch()