Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import json
+
+# ============================================================
+# 📦 LOAD MODEL
+# ============================================================
+
+MODEL_PATH = "animal_model.keras"
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# ============================================================
+# 📂 LOAD CLASS LABELS
+# ============================================================
+
+with open("class_labels.json", "r") as f:
+    class_labels = json.load(f)
+
+class_names = list(class_labels.keys())
+
+# ============================================================
+# 🖥️ STREAMLIT UI
+# ============================================================
+
+st.title("🐾 Animal Classification App")
+st.write("Upload an image and the model will predict the animal.")
+
+uploaded_file = st.file_uploader("Choose an image", type=["jpg", "png", "jpeg"])
+
+IMG_SIZE = (160, 160)
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    # Preprocess
+    img = image.resize(IMG_SIZE)
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Prediction
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+
+    st.subheader("🔍 Prediction:")
+    st.success(predicted_class)