app.py

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.file_uploader("Upload a wheat leaf image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    st.subheader("Uploaded Image")
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_container_width=True, width=500)

    # Preprocess the image
    processed_image = preprocess_image(image)

    # 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 = ['Healthy', 'Mild Rust (MR)', 'Moderate Rust (MRMS)', 'Severe Rust (MS)', 'Very Severe Rust (R)', 'Extremely Severe Rust (S)']

        st.header("Predicted Severity Class")
        
        # Colorful headings and styled responses
        if predicted_class == 0:
            st.markdown('<p style="color: #28a745; 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: #FFA500; font-size: 22px; font-weight: bold;">Mild Rust (MR)</p>', unsafe_allow_html=True)
            st.write("Mild rust detected.")
            st.write("1. Apply fungicides to control rust growth.")
            st.write("2. Regularly monitor the leaf for further signs of infection.")
        elif predicted_class == 2:
            st.markdown('<p style="color: #FF6347; font-size: 22px; font-weight: bold;">Moderate Rust (MRMS)</p>', unsafe_allow_html=True)
            st.write("Moderate rust detected.")
            st.write("1. Continue monitoring the leaf for any progression.")
            st.write("2. Treat with fungicides as required.")
        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.")
            st.write("1. Apply fungicides promptly to control rust spread.")
            st.write("2. Ensure regular monitoring to prevent further spread.")
        elif predicted_class == 4:
            st.markdown('<p style="color: #dc3545; font-size: 22px; font-weight: bold;">Very Severe Rust (R)</p>', unsafe_allow_html=True)
            st.write("Very severe rust detected.")
            st.write("1. Implement intensive control measures.")
            st.write("2. Apply fungicides multiple times.")
            st.write("3. Frequent monitoring is essential.")
        elif predicted_class == 5:
            st.markdown('<p style="color: #8B0000; font-size: 22px; font-weight: bold;">Extremely Severe Rust (S)</p>', unsafe_allow_html=True)
            st.write("Extremely severe rust detected.")
            st.write("1. Apply aggressive control strategies.")
            st.write("2. Seek expert advice for advanced interventions.")
            st.write("3. Frequent, close monitoring is critical.")

        confidence = np.max(prediction) * 100
        st.markdown(f'<p style="color: #17a2b8; font-size: 18px;">**Confidence Level:** {confidence:.2f}%</p>', unsafe_allow_html=True)

# Footer
st.info("MPHIL Final Year Project By Mr. Asim Khattak")