Create 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 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]
+
+# 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=(224, 224))  # Resize image to match model input
+    img = img_to_array(img)  # Convert to array
+    img = preprocess_input(img)  # Preprocess image for ResNet50
+
+    # Make prediction
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    features = model.predict(img)  # Extract features using the model
+    prediction = model.predict(features)  # Get prediction
+
+    # Show the result
+    predicted_class = class_names[prediction[0]]  # Get the class name
+    st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
+    st.write(f"Predicted Class: {predicted_class}")