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| import os | |
| import streamlit as st | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| from PIL import Image | |
| # Set environment variable for protobuf compatibility | |
| os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' | |
| # Define class names directly | |
| class_names = ['freshapples', 'freshbanana', 'freshcucumber', 'freshokra', 'freshoranges', | |
| 'freshpotato', 'freshtomato', 'rottenapples', 'rottenbanana', 'rottencucumber', | |
| 'rottenokra', 'rottenoranges', 'rottenpotato', 'rottentomato'] | |
| # Function to load the model | |
| def load_model(): | |
| try: | |
| model = tf.keras.models.load_model('final_freshness_resnet_model.keras') | |
| return model | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| return None | |
| # Prediction function | |
| def predict_freshness(img): | |
| # Load the model | |
| model = load_model() | |
| if model is None: | |
| return None, None | |
| # Preprocess the image | |
| img_resized = img.resize((224, 224)) | |
| img_array = image.img_to_array(img_resized) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array = tf.keras.applications.resnet50.preprocess_input(img_array) | |
| # Make prediction | |
| predictions = model.predict(img_array) | |
| predicted_class = np.argmax(predictions, axis=1)[0] | |
| confidence_score = predictions[0][predicted_class] * 100 | |
| return class_names[predicted_class], round(confidence_score, 2) | |
| # Streamlit app main function | |
| def main(): | |
| st.set_page_config(page_title="Freshness Detection System", page_icon=":apple:") | |
| st.title("🍎 Freshness Detection System") | |
| st.write("Upload an image to check its freshness.") | |
| # File uploader | |
| 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) | |
| # Predict freshness | |
| predicted_label, confidence = predict_freshness(image) | |
| # Display results | |
| if predicted_label is not None: | |
| st.subheader("Prediction Result:") | |
| st.write(f"**Category:** {predicted_label}") | |
| st.write(f"**Confidence Score:** {confidence}%") | |
| # Highlight based on prediction | |
| if "fresh" in predicted_label.lower(): | |
| st.success(f"✅ The food item is fresh!") | |
| else: | |
| st.warning(f"⚠️ The food item is rotten!") | |
| if __name__ == '__main__': | |
| main() |