app.py

import streamlit as st
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from PIL import Image

# Load the updated Keras model
model = load_model("clean_model.keras")

st.title("Disease Prediction App")
st.write("Upload an image and get prediction")

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

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

    # Preprocess image
    img = img.resize((64, 64))  # Replace with your model's expected input size
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0) / 255.0

    # Predict
    prediction = model.predict(img_array)

    # Result
    if prediction[0][0] > 0.5:
        st.write("🔴 Disease Detected")
    else:
        st.write("🟢 No Disease Detected")