app.py

import streamlit as st
from PIL import Image
import numpy as np
import tensorflow as tf
from tensorflow import keras

# Load the saved model
loaded_model = keras.models.load_model('tuned_model_classic.h5')

# Define the class labels (you can customize these according to your problem)
class_labels = ['Stroke', 'Non-Stroke']

# Streamlit App
st.title('Stroke Classifier')
st.write('This an app developed with collaboration of a doctor and CS student for the purpose of addressing stroke people faster in Bangladesh using AI, I used a classic CNN Architect')
st.write('Upload an image to classify')

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

if uploaded_image is not None:
    # Read the image and preprocess it
    image = Image.open(uploaded_image)
    image = image.convert('RGB')
    image = image.resize((150, 150))  # Resize to match the model's input shape
    image = np.array(image)  # Convert PIL image to numpy array
    image = image / 255.0  # Normalize pixel values (similar to how you did in the model training)

    # Make prediction using the loaded model
    prediction = loaded_model.predict(np.expand_dims(image, axis=0))[0]
    predicted_class_index = np.argmax(prediction)
    predicted_class = class_labels[predicted_class_index]
    confidence = prediction[predicted_class_index]

    # Display the uploaded image and the prediction
    st.image(image, caption=f'Uploaded Image', use_column_width=True)
    
    # Check if the predicted class is "Non-Stroke" and the confidence is high (you can adjust the threshold)
    if predicted_class == 'Non-Stroke' and confidence > 0.8:
        st.write(f'Predicted Class: Uncertain (Possibly both Stroke and Non-Stroke) (Confidence: {confidence:.2f})')
    else:
        st.write(f'Predicted Class: {predicted_class} (Confidence: {confidence:.2f})')