initial commit

app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+model = tf.keras.models.load_model('animal_classifier_model.h5')
+
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+st.title('Animal Classifier')
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    image = image.resize((32, 32))
+    image_array = np.array(image) / 255.0  
+    image_array = np.expand_dims(image_array, axis=0)  
+
+    predictions = model.predict(image_array)
+    score = tf.nn.softmax(predictions[0])
+
+    st.write(f"Prediction: {class_names[np.argmax(score)]}")
+    st.write(f"Confidence: {100 * np.max(score):.2f}%")