Create app.py

app.py

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+# app.py
+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.preprocessing import image as keras_image
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+import joblib
+
+# Load model and class names
+model = joblib.load("knn_model.pkl")
+class_names = np.load("class_names.npy")
+
+# Load feature extractor
+feature_extractor = MobileNetV2(weights='imagenet', include_top=False, pooling='avg')
+
+# Streamlit UI
+st.title("🐾 Animal Image Classifier")
+st.write("Upload an animal image and get the predicted class.")
+
+uploaded_file = st.file_uploader("Choose an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess image
+    img_resized = img.resize((224, 224))
+    img_array = keras_image.img_to_array(img_resized)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+
+    # Extract features
+    features = feature_extractor.predict(img_array)
+
+    # Predict
+    prediction = model.predict(features)[0]
+    st.success(f"🧠 Predicted Animal: **{prediction}**")