Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+from tensorflow.keras.preprocessing import image
+import os
+from PIL import Image
+
+# Load the pre-trained KNN model and class names
+knn = joblib.load('knn_model.pk1')
+class_names = joblib.load('class_names.pk1')
+
+# Title of the app
+st.title("Animal Classification Using KNN Model")
+
+# Description
+st.write("Upload an image of an animal and the model will predict which animal it is.")
+
+# Upload image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Display image
+    img = Image.open(uploaded_image)
+    st.image(img, caption='Uploaded Image.', use_column_width=True)
+
+    # Preprocess the image for prediction
+    img = img.resize((64, 64))  # Resize the image to match the model's expected size (adjust if needed)
+    img_array = np.array(img)  # Convert the image to numpy array
+    img_array = img_array.flatten().reshape(1, -1)  # Flatten the image and reshape it to match the input for KNN model
+
+    # Make prediction
+    prediction = knn.predict(img_array)
+    predicted_class = class_names[prediction[0]]
+
+    # Display prediction
+    st.write(f"Prediction: {predicted_class}")
+