Update app.py

app.py

@@ -1,76 +1,67 @@
-# # Import libraries
-# import gradio as gr
-# import tensorflow as tf
-# import numpy as np
-
-# # Initialize the number of classes, also the image's height and width
-# num_classes = 200
-# IMG_HEIGHT = 300
-# IMG_WIDTH = 300
+# Import libraries
+import gradio as gr
+import tensorflow as tf
+import numpy as np
 
-# # Open the classlabel.txt to read the class labels
-# with open("classlabel.txt", 'r') as file:
-#     CLASS_LABEL = [x.strip() for x in file.readlines()]
+# Initialize the number of classes, also the image's height and width
+num_classes = 200
+IMG_HEIGHT = 300
+IMG_WIDTH = 300
 
-# # Function to normalize the image
-# def normalize_image(img):
-#     img = tf.cast(img, tf.float32) / 255.0
-#     img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH), method='bilinear')
-#     return img
+# Open the classlabel.txt to read the class labels
+with open("classlabel.txt", 'r') as file:
+    CLASS_LABEL = [x.strip() for x in file.readlines()]
 
-# # Function to select and load the model
-# def load_model(model_name):
-#     # Load the model based on the model_name input
-#     if model_name == "Xception":
-#         return tf.keras.models.load_model("model/Xception.h5")
-#     elif model_name == "InceptionV3":
-#         return tf.keras.models.load_model("model/InceptionV3.h5")
-#     elif model_name == "InceptionResNetV2":
-#         return tf.keras.models.load_model("model/InceptionResNetV2.h5")
-#     elif model_name == "DenseNet201":
-#         return tf.keras.models.load_model("model/DenseNet201.h5")
-#     else:
-#         raise ValueError("Invalid model_name")
+# Function to normalize the image
+def normalize_image(img):
+    img = tf.cast(img, tf.float32) / 255.0
+    img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH), method='bilinear')
+    return img
 
-# # Main function, let the model make the prediction on the image uploaded
-# def predict_top_classes(img, model_name):
-#     img = img.convert('RGB')
-#     img_data = normalize_image(img)
-#     x = np.array(img_data)
-#     x = np.expand_dims(x, axis=0)
-#     model = load_model(model_name)
-#     temp = model.predict(x)
+# Function to select and load the model
+def load_model(model_name):
+    # Load the model based on the model_name input
+    if model_name == "Xception":
+        return tf.keras.models.load_model("model/Xception.h5")
+    elif model_name == "InceptionV3":
+        return tf.keras.models.load_model("model/InceptionV3.h5")
+    elif model_name == "InceptionResNetV2":
+        return tf.keras.models.load_model("model/InceptionResNetV2.h5")
+    elif model_name == "DenseNet201":
+        return tf.keras.models.load_model("model/DenseNet201.h5")
+    else:
+        raise ValueError("Invalid model_name")
 
-#     idx = np.argsort(np.squeeze(temp))[::-1]
-#     top5_value = np.asarray([temp[0][i] for i in idx[0:5]])
-#     top5_idx = idx[0:5]
-    
-#     # Return the top 5 highest probability class labels
-#     return {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
+# Main function, let the model make the prediction on the image uploaded
+def predict_top_classes(img, model_name):
+    img = img.convert('RGB')
+    img_data = normalize_image(img)
+    x = np.array(img_data)
+    x = np.expand_dims(x, axis=0)
+    model = load_model(model_name)
+    temp = model.predict(x)
 
-# # Define the interface
-# interface = gr.Interface(
-#     predict_top_classes,
-#     [
-#         gr.inputs.Image(type='pil'),
-#         gr.inputs.Dropdown(
-#             choices=["Xception","InceptionV3","InceptionResNetV2","DenseNet201"],
-#             type="value",
-#             label="Select a model",
-#             info="Base model that done feature extraction and fine-tuning process"
-#         )
-#     ],
-#     outputs='label'
-# )
+    idx = np.argsort(np.squeeze(temp))[::-1]
+    top5_value = np.asarray([temp[0][i] for i in idx[0:5]])
+    top5_idx = idx[0:5]
     
-# # Launch the interface
-# interface.launch()
-
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!"
+    # Return the top 5 highest probability class labels
+    return {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# Define the interface
+interface = gr.Interface(
+    predict_top_classes,
+    [
+        gr.inputs.Image(type='pil'),
+        gr.inputs.Dropdown(
+            choices=["Xception","InceptionV3","InceptionResNetV2","DenseNet201"],
+            type="value",
+            label="Select a model",
+            info="Base model that done feature extraction and fine-tuning process"
+        )
+    ],
+    outputs='label'
+)
     
-demo.launch()   
+# Launch the interface
+interface.launch()