Update App.py

App.py

@@ -3,20 +3,18 @@ from transformers import pipeline
 
 classifier = pipeline("image-classification", model="Asseh/Ball_Classification")
 
-# Function to classify images into 7 classes
-def image_classifier(inp):
-    # Dummy classification logic
-    # Generating random confidence scores for each class
-    confidence_scores = np.random.rand(7)
-    # Normalizing confidence scores to sum up to 1
-    confidence_scores /= np.sum(confidence_scores)
-    # Creating a dictionary with class labels and corresponding confidence scores
-    classes = ['american_football', 'baseball', 'basketball', 'billiard_ball', 'bowling_ball','cricket_ball', 'football', 'golf_ball', 'hockey_ball', 'hockey_puck', 'rugby_ball', 'shuttlecock', 'table_tennis_ball', 'tennis_ball', 'volleyball']
-    result = {classes[i]: confidence_scores[i] for i in range(15)}
-    return result
+def predict(input_img):
+  predictions = classifier(input_img)
+  return input_img, {p["label"]: p["score"] for p in predictions}
 
-# Creating Gradio interface
-demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
 
-if name == "main":
-    demo.launch()
+gradio_app = gr.Interface(
+    predict,
+    inputs = gr.Image(label = "Select Image", sources=['upload', 'webcam'], type="pil"),
+    outputs = [gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=15)],
+
+    title="Which type of ball"
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()