Update app.py

app.py

@@ -1,67 +1,76 @@
-# Import libraries
-import gradio as gr
-import tensorflow as tf
-import numpy as np
+# # 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
 
-# Initialize the number of classes, also the image's height and width
-num_classes = 200
-IMG_HEIGHT = 300
-IMG_WIDTH = 300
+# # 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()]
 
-# 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 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
 
-# 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
+# # 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 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")
+# # 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)
 
-# 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)
+#     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)}
 
-    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]
+# # 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'
+# )
     
-    # Return the top 5 highest probability class labels
-    return {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
+# # Launch the interface
+# interface.launch()
+
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!"
 
-# 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 = gr.Interface(fn=greet, inputs="text", outputs="text")
     
-# Launch the interface
-interface.launch()
+demo.launch()