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| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.image import img_to_array # Import this function | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| from huggingface_hub import hf_hub_download, login | |
| # Title of the Streamlit app | |
| st.title("Yellow Rust Severity Prediction") | |
| # Authentication using Hugging Face token | |
| authkey = os.getenv('YellowRust') | |
| login(token=authkey) | |
| # Download the model file from Hugging Face | |
| model_path = hf_hub_download(repo_id="shaheer-data/Yellow-Rust-Prediction", filename="final_meta_model.keras") | |
| # Load the pre-trained model | |
| loaded_model = load_model(model_path) | |
| # Function to preprocess the uploaded image | |
| def preprocess_image(image): | |
| # Resize the image to match the model input size (e.g., 224x224 for many pre-trained models) | |
| image = image.resize((224, 224)) # Adjust size based on your model input | |
| 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 | |
| # Streamlit file uploader | |
| uploaded_file = st.sidebar.file_uploader("Upload a wheat leaf image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| st.sidebar.subheader("Uploaded Image") | |
| image = Image.open(uploaded_file) | |
| st.sidebar.image(image, caption="Uploaded Image", use_container_width=True) | |
| # Preprocess the image | |
| processed_image = preprocess_image(image) | |
| st.subheader("Prediction: With 97% Accuracy") | |
| # Perform prediction | |
| with st.spinner("Predicting..."): | |
| prediction = loaded_model.predict(processed_image) | |
| predicted_class = np.argmax(prediction, axis=1)[0] # Get the class index | |
| class_labels = ['0', 'MR', 'MRMS', 'MS', 'R', 'S'] # Update based on your classes | |
| st.header("Predicted Severity Class") | |
| if predicted_class == 0: | |
| st.markdown('<p style="color: green; font-size: 20px;">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: 20px;">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: 20px;">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: 20px;">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: 20px;">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: 20px;">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.write(f"**Confidence Level:** {confidence:.2f}%") | |
| # Footer | |
| st.info("MPHIL Final Year Project By Mr. Asim Khattak") | |