Abdullah-Basar/Animal_Classification

1import streamlit as st2import joblib3import numpy as np4from sklearn.neighbors import KNeighborsClassifier5from tensorflow.keras.preprocessing import image6import os7from PIL import Image8 9# Load the pre-trained KNN model and class names10knn = joblib.load('knn_model.pk1')11class_names = joblib.load('class_names.pk1')12 13# Title of the app14st.title("Animal Classification Using KNN Model")15 16# Description17st.write("Upload an image of an animal and the model will predict which animal it is.")18 19# Upload image20uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])21 22if uploaded_image is not None:23    # Display image24    img = Image.open(uploaded_image)25    st.image(img, caption='Uploaded Image.', use_column_width=True)26 27    # Preprocess the image for prediction28    img = img.resize((64, 64))  # Resize the image to match the model's expected size (adjust if needed)29    img_array = np.array(img)  # Convert the image to numpy array30    img_array = img_array.flatten().reshape(1, -1)  # Flatten the image and reshape it to match the input for KNN model31 32    # Make prediction33    prediction = knn.predict(img_array)34    predicted_class = class_names[prediction[0]]35 36    # Display prediction37    st.write(f"Prediction: {predicted_class}")38 39