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| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from huggingface_hub import hf_hub_download | |
| from io import BytesIO | |
| st.title("Heart Arrhythmia Detection Tools (hadt)") | |
| st.markdown(""" | |
| This is a demo of the Heart Arrhythmia Detection Tools (hadt) project. | |
| The project is available on [GitHub](https://github.com/fabriciojm/hadt). | |
| """) | |
| models = { | |
| "LSTM Multiclass": "lstm_multi_model.h5", | |
| "CNN Multiclass": "cnn_multi_model.h5", | |
| "PCA XGBoost Multiclass": "pca_xgboost_multi_model.pkl", | |
| "LSTM Binary": "lstm_binary_model.h5", | |
| "CNN Binary": "cnn_binary_model.h5", | |
| "PCA XGBoost Binary": "pca_xgboost_binary_model.pkl", | |
| } | |
| beat_labels = { | |
| "N": "Normal", | |
| "Q": "Unknown Beat", | |
| "S": "Supraventricular Ectopic", | |
| "V": "Ventricular Ectopic", | |
| "A": "Abnormal", | |
| } | |
| # Model selection | |
| classification = ["Multiclass", "Binary"] | |
| model_list = ["LSTM", "CNN", "PCA XGBoost"] | |
| model_selected = st.selectbox("Select a Model", model_list) | |
| classification_selected = st.selectbox("Classification type", classification) | |
| model_name = f"{model_selected} {classification_selected}" | |
| st.markdown("""Upload a CSV file with single heartbeat (csv with 180 points) or load from available examples | |
| """) | |
| # Option to upload or load a file | |
| option = st.radio("Choose input method", ("Load example file", "Upload CSV file")) | |
| if option == "Load example file": | |
| # Load example files from Hugging Face dataset | |
| example_files = ["single_N.csv", "single_Q.csv", "single_S.csv", "single_V.csv"] | |
| example_selected = st.selectbox("Select an example file", example_files) | |
| # Load the selected example file | |
| file_path = hf_hub_download(repo_id='fabriciojm/ecg-examples', repo_type='dataset', filename=example_selected) | |
| with open(file_path, 'rb') as f: | |
| file_content = f.read() | |
| uploaded_file = BytesIO(file_content) | |
| uploaded_file.name = example_selected # Set a name attribute to mimic the uploaded file | |
| df = pd.read_csv(uploaded_file) | |
| # st.write("Loaded Data:", df) | |
| else: | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload a CSV file", type="csv") | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file) | |
| # st.write("Uploaded Data:", df) | |
| # Visualize data | |
| if 'df' in locals(): | |
| st.write("Visualized Data:") | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| df.iloc[0].plot(ax=ax) | |
| st.pyplot(fig) | |
| if st.button("Predict"): | |
| model = models[model_name] | |
| # Reset the file pointer to the beginning | |
| uploaded_file.seek(0) | |
| # Call the API with the file directly | |
| response = requests.post( | |
| f"https://fabriciojm-hadt-api.hf.space/predict?model_name={model}", | |
| files={"filepath_csv": (uploaded_file.name, uploaded_file, "text/csv")} | |
| ) | |
| if response.status_code == 200: | |
| prediction = response.json()["prediction"] | |
| st.write(f"Prediction using {model_name}: {beat_labels[prediction]} (class {prediction}) heartbeat") | |
| else: | |
| st.error(f"Error: {response.json().get('detail', 'Unknown error')}") | |