Update app.py

app.py

@@ -31,7 +31,7 @@ model_file = download_model()
 # Load the model
 model = tf.keras.models.load_model(model_file)
 
-# Perform image classification
+# Perform image classification for single class output
 # def predict_class(image):
 #     img = tf.cast(image, tf.float32)
 #     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
@@ -41,6 +41,7 @@ model = tf.keras.models.load_model(model_file)
 #     predicted_class = labels[class_index]
 #     return predicted_class
 
+# Perform image classification for multy class output
 def predict_class(image):
     img = tf.cast(image, tf.float32)
     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
@@ -48,26 +49,22 @@ def predict_class(image):
     prediction = model.predict(img)
     return prediction[0]
 
-# UI Design
+# UI Design for single class output
 # def classify_image(image):
 #     predicted_class = predict_class(image)
 #     output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
 #     return output
 
+
+# UI Design for multy class output
 def classify_image(image):
     results = predict_class(image)
-    # output = {}
-    # for index in range(len(results)):
-    #     predicted_label = labels.get(index)
-    #     score = results[index]
-    #     output[predicted_label] = str(score)
     output = {labels.get(i): float(results[i]) for i in range(len(results))}
     return output
 
 
-
 inputs = gr.inputs.Image(type="pil", label="Upload an image")
-# outputs = gr.outputs.HTML()
+# outputs = gr.outputs.HTML() #uncomment for single class output 
 outputs = gr.outputs.Label(num_top_classes=5)
 
 title = "<h1 style='text-align: center;'>Image Classifier</h1>"