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| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| from huggingface_hub import hf_hub_download, login | |
| # Set page configuration | |
| st.set_page_config(page_title="Yellow Rust Severity Prediction", layout="wide", initial_sidebar_state="expanded") | |
| # main content | |
| st.markdown('<p style="text-align: center; color: #eaf26f; font-size: 40px; font-weight: bold;">Yellow Rust Severity Prediction Dashboard</p>', unsafe_allow_html=True) | |
| # Authentication using Hugging Face token | |
| authkey = os.getenv('YellowRust') | |
| login(token=authkey) | |
| model_id = os.getenv('model_repo_id') | |
| model_file = os.getenv('model_path') | |
| # Download the model file from Hugging Face | |
| model_path = hf_hub_download(repo_id=model_id, filename=model_file) | |
| # Load the pre-trained model | |
| loaded_model = load_model(model_path) | |
| # Function to preprocess the uploaded image | |
| def preprocess_image(image): | |
| image = image.resize((224, 224)) # Resize to match model input size | |
| image = img_to_array(image) # Convert image to numpy array | |
| image = image / 255.0 # Normalize pixel values to [0, 1] | |
| image = np.expand_dims(image, axis=0) # Add batch dimension | |
| return image | |
| # Sidebar layout with a colorful menu | |
| st.sidebar.markdown('<p style="font-size: 24px; color: #2F4F4F; font-weight: bold;">Yellow Rust Prediction</p>', unsafe_allow_html=True) | |
| st.sidebar.markdown('<p style="color: #555;">Upload an image of the wheat leaf to predict the severity of yellow rust.</p>', unsafe_allow_html=True) | |
| # Sidebar elements | |
| uploaded_file = st.sidebar.file_uploader("Upload Wheat Leaf Image", type=["jpg", "jpeg", "png"]) | |
| st.sidebar.markdown("---") | |
| # Display the uploaded image | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.sidebar.image(image, caption="Uploaded Wheat Leaf", use_container_width=True) | |
| # Preprocess the image | |
| processed_image = preprocess_image(image) | |
| # Predict severity with a spinner | |
| with st.spinner("Predicting..."): | |
| prediction = loaded_model.predict(processed_image) | |
| predicted_class = np.argmax(prediction, axis=1)[0] # Get the class index | |
| class_labels = ['Healthy', 'Mild Rust (MR)', 'Moderate Rust (MRMS)', 'Severe Rust (MS)', 'Very Severe Rust (R)', 'Extremely Severe Rust (S)'] | |
| st.header("Predicted Severity Class") | |
| # Conditional statements for displaying the prediction with styled headers and colors | |
| if predicted_class == 0: | |
| st.markdown('<p style="color: green; font-size: 22px; font-weight: bold;">Healthy</p>', unsafe_allow_html=True) | |
| st.write("The leaf appears healthy. There is no immediate action required. Continue monitoring as needed.") | |
| elif predicted_class == 1: | |
| st.markdown('<p style="color: orange; font-size: 22px; font-weight: bold;">Mild Rust (MR)</p>', unsafe_allow_html=True) | |
| st.write("Mild rust detected. Applying fungicides will help control further spread.") | |
| elif predicted_class == 2: | |
| st.markdown('<p style="color: #FFA500; font-size: 22px; font-weight: bold;">Moderate Rust (MRMS)</p>', unsafe_allow_html=True) | |
| st.write("Moderate rust detected. Monitor regularly and treat with fungicides.") | |
| elif predicted_class == 3: | |
| st.markdown('<p style="color: #FF4500; font-size: 22px; font-weight: bold;">Severe Rust (MS)</p>', unsafe_allow_html=True) | |
| st.write("Severe rust detected. Prompt fungicide application and continued monitoring are recommended.") | |
| elif predicted_class == 4: | |
| st.markdown('<p style="color: red; font-size: 22px; font-weight: bold;">Very Severe Rust (R)</p>', unsafe_allow_html=True) | |
| st.write("Very severe rust detected. Intensive control measures and frequent monitoring are required.") | |
| elif predicted_class == 5: | |
| st.markdown('<p style="color: darkred; font-size: 22px; font-weight: bold;">Extremely Severe Rust (S)</p>', unsafe_allow_html=True) | |
| st.write("Extremely severe rust detected. Apply aggressive control strategies and seek expert advice.") | |
| confidence = np.max(prediction) * 100 | |
| st.markdown(f'<p style="color: #17a2b8; font-size: 18px; font-weight: bold;">Confidence Level: {confidence:.2f}%</p>', unsafe_allow_html=True) | |
| # Footer | |
| st.info("MPHIL Final Year Project By Mr. Asim Khattak", icon="📚") | |