app.py

import streamlit as st
from transformers import pipeline
import requests
from io import BytesIO
from PIL import Image

# Define models and their validation accuracies
model_options = {
    "Model Name": {
        "path": "model_name.h5",
        "accuracy": 50
    },
    "Old Model": {
        "path": "oldModel.h5",
        "accuracy": 76
    }
}

# Load the model from Hugging Face repo
def load_model(model_path):
    # Here you would use the Hugging Face `transformers` library to load your model.
    # However, since these are `.h5` models (likely Keras models), use the appropriate loader.
    # This example assumes you have a custom loader function for Keras models.
    from tensorflow.keras.models import load_model
    return load_model(model_path)

def main():
    st.title("Pneumonia Detection App")

    model_name = st.selectbox("Select a model", list(model_options.keys()))
    model_path = model_options[model_name]["path"]
    model_accuracy = model_options[model_name]["accuracy"]

    # Load the selected model
    model = load_model(model_path)
    
    st.write(f"Model: {model_name}")
    st.write(f"Validation Accuracy: {model_accuracy}%")

    uploaded_file = st.file_uploader("Upload 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)

        # Perform prediction using the model
        # This part depends on how your model expects input.
        # Here, you would preprocess the image and perform prediction.
        # For example:
        # img_array = preprocess_image(image)
        # prediction = model.predict(img_array)
        # st.write("Prediction:", prediction)

        # Example placeholder for prediction output
        st.write("Prediction: [Placeholder for actual prediction]")

if __name__ == "__main__":
    main()