Update app.py

app.py

@@ -1,55 +1,67 @@
+# 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
-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()
+
+
+def greet(name):
+    return "Hello " + name
+
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+demo.launch()