Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from PIL import Image
+import os
+
+# Load the trained model (make sure the file is in the same directory)
+model = tf.keras.models.load_model("animal_classifier.keras")
+
+# Define class names (same order as training)
+train_dir = "split_animals/train"
+class_names = sorted(os.listdir(train_dir))
+
+# Prediction function
+def predict_image(img):
+    img = img.resize((224, 224))
+    img_array = tf.keras.utils.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    prediction = model.predict(img_array)
+    predicted_class = class_names[np.argmax(prediction)]
+    return f"Predicted Animal: {predicted_class}"
+
+# Launch Gradio app
+app = gr.Interface(fn=predict_image, inputs=gr.Image(type="pil"), outputs="text", title="🧠🐾 TF-WildNet: Animal Classifier")
+app.launch()