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Create app.py

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  1. app.py +58 -0
app.py ADDED
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+ import streamlit as st
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+ from transformers import pipeline
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+ import requests
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+ from io import BytesIO
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+ from PIL import Image
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+
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+ # Define models and their validation accuracies
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+ model_options = {
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+ "Model Name": {
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+ "path": "model_name.h5",
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+ "accuracy": 50
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+ },
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+ "Old Model": {
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+ "path": "oldModel.h5",
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+ "accuracy": 76
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+ }
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+ }
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+
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+ # Load the model from Hugging Face repo
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+ def load_model(model_path):
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+ # Here you would use the Hugging Face `transformers` library to load your model.
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+ # However, since these are `.h5` models (likely Keras models), use the appropriate loader.
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+ # This example assumes you have a custom loader function for Keras models.
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+ from tensorflow.keras.models import load_model
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+ return load_model(model_path)
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+
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+ def main():
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+ st.title("Pneumonia Detection App")
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+
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+ model_name = st.selectbox("Select a model", list(model_options.keys()))
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+ model_path = model_options[model_name]["path"]
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+ model_accuracy = model_options[model_name]["accuracy"]
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+
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+ # Load the selected model
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+ model = load_model(model_path)
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+
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+ st.write(f"Model: {model_name}")
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+ st.write(f"Validation Accuracy: {model_accuracy}%")
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+
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+ uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
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+
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+ if uploaded_file is not None:
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+ image = Image.open(uploaded_file)
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+ st.image(image, caption="Uploaded Image", use_column_width=True)
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+
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+ # Perform prediction using the model
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+ # This part depends on how your model expects input.
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+ # Here, you would preprocess the image and perform prediction.
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+ # For example:
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+ # img_array = preprocess_image(image)
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+ # prediction = model.predict(img_array)
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+ # st.write("Prediction:", prediction)
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+
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+ # Example placeholder for prediction output
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+ st.write("Prediction: [Placeholder for actual prediction]")
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+
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+ if __name__ == "__main__":
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+ main()