Created
December 25, 2024 01:32
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| import streamlit as st | |
| from PIL import Image | |
| import tensorflow as tf | |
| import numpy as np | |
| # Load pre-trained MobileNetV2 model | |
| @st.cache_resource | |
| def load_model(): | |
| model = tf.keras.applications.MobileNetV2(weights='imagenet') | |
| return model | |
| model = load_model() | |
| # Function to preprocess the image | |
| def preprocess_image(image): | |
| img = image.resize((224, 224)) # Resize to match model input size | |
| img_array = tf.keras.preprocessing.image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array) | |
| return img_array | |
| # Function to predict the animal name | |
| def predict_animal(image): | |
| processed_image = preprocess_image(image) | |
| predictions = model.predict(processed_image) | |
| decoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=1) | |
| return decoded_predictions[0][0][1] # Get the predicted label (class name) | |
| # Streamlit app | |
| st.title("Animal Classifier") | |
| st.write("Upload an image, and the app will identify the animal!") | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file is not None: | |
| # Display the uploaded image | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption='Uploaded Image', use_column_width=True) | |
| st.write("Classifying...") | |
| # Predict the animal name | |
| prediction = predict_animal(image) | |
| st.write(f"Prediction: **{prediction}**") |
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