Update app.py

app.py

@@ -33,43 +33,42 @@ model_file = download_model()
 model = tf.keras.models.load_model(model_file)
 
 # 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]])
-    img = tf.expand_dims(img, axis=0)
-    prediction = model.predict(img)
-    class_index = tf.argmax(prediction[0]).numpy()
-    predicted_class = labels[class_index]
-    print("predicted_class is ", predicted_class)####################################################
-    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]])
 #     img = tf.expand_dims(img, axis=0)
 #     prediction = model.predict(img)
-#     return prediction[0]
+#     class_index = tf.argmax(prediction[0]).numpy()
+#     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]])
+    img = tf.expand_dims(img, axis=0)
+    prediction = model.predict(img)
+    return prediction[0]
 
 # 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
+# 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)
-#     print(results)
-#     output = {labels.get(i): float(results[i]) for i in range(len(results))}
-#     result = output if max(output.values()) >=0.98 else {"NO_CIFAR10_CLASS": 1}
-#     return result
+def classify_image(image):
+    results = predict_class(image)
+    print(results)
+    output = {labels.get(i): float(results[i]) for i in range(len(results))}
+    result = output if max(output.values()) >=0.98 else {"NO_CIFAR10_CLASS": 1}
+    return result
 
 
 inputs = gr.inputs.Image(type="pil", label="Upload an image")
-outputs = gr.outputs.HTML() #uncomment for single class output 
-#outputs = gr.outputs.Label(num_top_classes=4)
+# outputs = gr.outputs.HTML() #uncomment for single class output 
+outputs = gr.outputs.Label(num_top_classes=4)
 
 title = "<h1 style='text-align: center;'>Image Classifier</h1>"
 description = "Upload an image and get the predicted class."
@@ -81,5 +80,4 @@ gr.Interface(fn=classify_image,
              title=title, 
              examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_house.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
              # css=css_code,
-             description=description,
-            enable_queue=True).launch()
+             description=description).launch()