app.py

import streamlit as st
import tensorflow as tf
from tf.keras.models import load_model
from PIL import Image
import os
from huggingface_hub import notebook_login
from huggingface_hub import hf_hub_download

# Title of the Streamlit app
st.title("Yellow Rust Severity Prediction")



# Downloading Model from hugging face url
st.write("Downloading model from Hugging Face repo:", model_repo_id)
# Download the model file from Hugging Face
model_path = hf_hub_download(repo_id="shaheer-data/Yellow-Rust-Prediction", filename="final_meta_model.keras")


# Construct the model URL
st.write("Loading model from Hugging Face repo:", model_repo_id)

loaded_model = load_model(model_path)  # Load model using tf.keras directly

# Function to make predictions
def predict_image(image):
    image = image.resize((224, 224))  # Resize to match model input dimensions
    image_array = tf.keras.preprocessing.image.img_to_array(image)
    image_array = tf.expand_dims(image_array, axis=0)  # Expand dimensions for batch prediction
    predictions = loaded_model.predict(image_array)
    return predictions

# Class labels for Yellow Rust severity levels
CLASS_LABELS = [
    "Healthy",
    "Mild Severity",
    "Moderate Severity",
    "Severe Severity",
    "Very Severe",
    "Extreme Severity"
]

# Image upload widget
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Display progress bar
    with st.spinner("Making predictions..."):
        predictions = predict_image(image)
        predicted_class = predictions.argmax(axis=-1)
        st.write(f"Predicted Severity Level: {CLASS_LABELS[predicted_class[0]]} with confidence {predictions[0][predicted_class[0]]:.2f}")

else:
    st.write("Please upload an image file to make predictions.")