Update app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
+# Load the trained KNN model and class names
+model = joblib.load('knn_model.joblib')
+with open('class_names.txt', 'r') as f:
+    class_names = f.readlines()
+class_names = [x.strip() for x in class_names]
+
+# Load pre-trained ResNet50 model for feature extraction
+resnet_model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
+
+# Streamlit app
+st.title('Animal Image Classifier')
+st.write('Upload an image to classify it.')
+
+# Upload Image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Process the image
+    img = load_img(uploaded_file, target_size=(32, 32))
+    img = img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input(img)
+
+    # Extract features
+    features = resnet_model.predict(img)
+
+    # Make prediction
+    prediction = model.predict(features)
+    predicted_class = class_names[prediction[0]]
+
+    # Display result
+    st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
+    st.write(f"Predicted Class: {predicted_class}")