Update app.py

app.py

@@ -1,67 +1,55 @@
-# import gradio as gr
-# import tensorflow as tf
-# import numpy as np
-
-# num_classes = 200
-# IMG_HEIGHT = 300
-# IMG_WIDTH = 300
-
-# with open("classlabel.txt", 'r') as file:
-#     CLASS_LABEL = [x.strip() for x in file.readlines()]
-
-# 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
-
-# def load_model(model_name):
-#     # Load the model based on the model_name input
-#     if model_name == "model1":
-#         return tf.keras.models.load_model("model/Xception.h5")
-#     elif model_name == "model2":
-#         return tf.keras.models.load_model("model/InceptionV3.h5")
-#     elif model_name == "model3":
-#         return tf.keras.models.load_model("model/InceptionResNetV2.h5")
-#     elif model_name == "model4":
-#         return tf.keras.models.load_model("model/DenseNet201.h5")
-#     else:
-#         raise ValueError("Invalid model_name")
-
-# 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 {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
-
-# interface = gr.Interface(
-#     predict_top_classes,
-#     [
-#         gr.inputs.Image(type='pil'),
-#         gr.inputs.Button(label="Model 1 (Xception)", value="model1"),
-#         gr.inputs.Button(label="Model 2 (InceptionV3)", value="model2"),
-#         gr.inputs.Button(label="Model 3 (InceptionResNetV2)", value="model3"),
-#         gr.inputs.Button(label="Model 4 (DenseNet201)", value="model4")
-#     ],
-#     outputs='label'
-# )
-# interface.launch()
-
-
 import gradio as gr
-
-
-def greet(name):
-    return "Hello " + name
-
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-demo.launch()
+import tensorflow as tf
+import numpy as np
+
+num_classes = 200
+IMG_HEIGHT = 300
+IMG_WIDTH = 300
+
+with open("classlabel.txt", 'r') as file:
+    CLASS_LABEL = [x.strip() for x in file.readlines()]
+
+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
+
+def load_model(model_name):
+    # Load the model based on the model_name input
+    if model_name == "model1":
+        return tf.keras.models.load_model("model/Xception.h5")
+    elif model_name == "model2":
+        return tf.keras.models.load_model("model/InceptionV3.h5")
+    elif model_name == "model3":
+        return tf.keras.models.load_model("model/InceptionResNetV2.h5")
+    elif model_name == "model4":
+        return tf.keras.models.load_model("model/DenseNet201.h5")
+    else:
+        raise ValueError("Invalid model_name")
+
+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 {CLASS_LABEL[i]: str(v) for i, v in zip(top5_idx, top5_value)}
+
+interface = gr.Interface(
+    predict_top_classes,
+    [
+        gr.inputs.Image(type='pil'),
+        gr.inputs.Button(label="Model 1 (Xception)", value="model1"),
+        gr.inputs.Button(label="Model 2 (InceptionV3)", value="model2"),
+        gr.inputs.Button(label="Model 3 (InceptionResNetV2)", value="model3"),
+        gr.inputs.Button(label="Model 4 (DenseNet201)", value="model4")
+    ],
+    outputs='label'
+)
+interface.launch()