Update app.py

app.py

@@ -1,27 +1,27 @@
-import gradio as gr
-import numpy as np
-from keras.models import load_model
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-
-# load model
-model = load_model("intel_model.keras")
-
-# class labels (same order as training)
-class_labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
-
-def predict(img):
-    img = img.resize((150,150,3))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-
-    pred = model.predict(img_array)
-    return class_labels[np.argmax(pred)]
-
-# UI
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="label",
-    title="Intel Image Classifier"
+import gradio as gr
+import numpy as np
+from keras.models import load_model
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# load model
+model = load_model("intel_model.keras")
+
+# class labels (same order as training)
+class_labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
+
+def predict(img):
+    img = img.resize((150,150))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    pred = model.predict(img_array)
+    return class_labels[np.argmax(pred)]
+
+# UI
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="label",
+    title="Intel Image Classifier"
 ).launch()