update ui

app.py

@@ -16,22 +16,33 @@ def predict_image(image):
 
   predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
 
-  # Top 3 classes
-  top_3_indices = np.argsort(predictions.numpy(), axis=1)[0][-3:][::-1]
-  top_3_labels = [labels[i] for i in top_3_indices]
-  top_3_probabilities = [predictions.numpy()[0][i] * 100 for i in top_3_indices]
-
-  output_string = "\n".join([f"{label}: {probability:.2f}%" for label, probability in zip(top_3_labels, top_3_probabilities)])
-
-  return image_resized, output_string
+  # Highest prediction
+  top_index = np.argmax(predictions.numpy(), axis=1)[0]
+  top_label = labels[top_index]
+  top_probability = predictions.numpy()[0][top_index] * 100
+
+  return top_label, top_probability  
+
+# Example images
+example_images = [
+    ["exp_img/cat.jpg"],
+    ["exp_img/cow.jpg"],
+    ["exp_img/elephant.jpg"],
+    ["exp_img/sheep.jpg"],
+    ["exp_img/spider.jpg"],
+    ["exp_img/squirrel.jpg"]
+]
 
 # Gradio Interface
 interface = gr.Interface(
     fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(type="pil", label="Image Output"), gr.Textbox(label="Prediction")],
+    inputs=gr.Image(type="pil", shape=(224, 224)),
+    outputs=gr.Label(num_top_classes=1, label="Prediction"),
+    examples=example_images,
+    example_per_page=6,
     title="Animals Classifier",
-    description="Upload an image of an animal, and the model will predict it.\n\n**Disclaimer:** This model is trained only on specific animal classes (butterfly, cats, cow, dogs, elephant, horse, monkey, sheep, spider, squirrel) and may not accurately predict animals outside these classes."
+    description="Upload an image of an animal, and the model will predict it.\n\n**Disclaimer:** This model is trained only on specific animal classes (butterfly, cats, cow, dogs, elephant, horse, monkey, sheep, spider, squirrel) and may not accurately predict animals outside these classes.",
+    allow_flagging="never"
 )
 
 interface.launch(share=True)