Update app.py

app.py

@@ -2,12 +2,13 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
+import json
 
-# Load your model from Hugging Face repo (or local file if testing)
+# Load model and class names JSON
 model = tf.keras.models.load_model("animal_classifier.keras")
 
-# Dynamically get class names from model output layer
-class_names = list(model.class_names) if hasattr(model, 'class_names') else ["dog", "lion", "tiger"]  # fallback if not stored in model
+with open("class_names.json", "r") as f:
+    class_names = json.load(f)
 
 def predict_image(image):
     img = image.resize((224, 224))
@@ -18,12 +19,14 @@ def predict_image(image):
     confidence = np.max(preds)
     predicted_index = np.argmax(preds)
 
-    threshold = 0.5
+    threshold = 0.5  # minimum confidence to accept prediction
+
     if confidence < threshold:
         return "Image not recognized as any animal in the dataset"
     else:
         return class_names[predicted_index]
 
-demo = gr.Interface(fn=predict_image, inputs=gr.Image(type="pil"), outputs="text", title="MobileNetV2 Animal Classifier")
+demo = gr.Interface(fn=predict_image, inputs=gr.Image(type="pil"), outputs="text",
+                    title="MobileNetV2 Animal Classifier")
 
 demo.launch()