app.py

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")

# 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):
    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("---")

# Main content
st.title("Yellow Rust Severity Prediction")
st.markdown('<p style="text-align: center; color: #2F4F4F; font-size: 30px; font-weight: bold;">Yellow Rust Severity Prediction Dashboard</p>', unsafe_allow_html=True)

# Display the uploaded image
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Wheat Leaf", use_column_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="📚")