Update app.py

app.py

@@ -1,30 +1,34 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
-from tensorflow.keras.utils import img_to_array  # ✅ fixed import
+from tensorflow.keras.utils import img_to_array
 import numpy as np
 from PIL import Image
 
 # Load the model
 model = load_model("model.h5")
 
-# Preprocessing function
+# Label list (edit this according to your model output)
+class_labels = ['Cat', 'Dog', 'Panda']  # Change these
+
+# Prediction function
 def predict_image(img):
-    img = img.resize((128, 128))  # Resize image to match model input
-    img_array = img_to_array(img)  # ✅ fixed line
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = img_array / 255.0  # Normalize
+    img = img.convert("RGB")  # Ensure RGB format
+    img = img.resize((128, 128))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
 
     prediction = model.predict(img_array)
     class_index = np.argmax(prediction)
-    
-    return f"Predicted Class: {class_index}"
 
-# Gradio interface
+    return f"Predicted Class: {class_labels[class_index]}"
+
+# Gradio UI
 interface = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Image Classification App"
+    title="Image Classifier"
 )
 
 interface.launch()