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import streamlit as st
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import pandas as pd
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from Utility.data_loader import (
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load_train_series, load_train_events,
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load_sample_submission, load_test_series
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)
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st.set_page_config(page_title="Sleep Detection", layout="wide")
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st.title("Sleep Detection")
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st.markdown("""
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### 📊 About the App
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This **Sleep Detection App** uses sensor data collected over time to predict sleep-related events such as *onset* or *wake-up*. The application allows users to analyze sleep patterns based on movement data and provides predictions using a machine learning model trained on labeled sensor events.
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---
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### 🧾 Data Description
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Each row in the dataset represents a time-stamped sensor reading with the following key columns:
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- **series_id**: Unique identifier for a sleep session or user.
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- **step**: Sequence number of the reading.
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- **sensor_timestamp**: The time when the sensor reading was recorded.
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- **anglez**: Z-axis body orientation angle (used as a feature).
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- **enmo**: Euclidean Norm Minus One – a movement magnitude metric (used as a feature).
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- **night**: Night identifier (used to separate sessions).
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- **event**: The sleep-related label (e.g., `onset`, `wake`) indicating the event type.
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- **event_timestamp**: Timestamp of the actual sleep event (used to calculate sleep duration).
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---
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### 🤖 App Capabilities
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- Displays raw sensor data and sleep event counts.
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- Trains an ML model (XGBoost) using movement features (`anglez`, `enmo`) to predict sleep events.
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- Allows real-time prediction of sleep events based on user input.
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- Displays evaluation metrics: **Accuracy**, **F1 Score**, **ROC AUC Score**.
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---
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""")
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st.header("Select Dataset to View")
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option = st.radio(
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"Choose a dataset:",
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("Train Events","Train Series", "Test Series", "Summary")
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)
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df = None
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if option == "Train Events":
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df = load_train_events()
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st.subheader("Train Events")
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st.dataframe(df.head())
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elif option == "Sample Submission":
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df = load_sample_submission()
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st.subheader("Sample Submission")
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st.dataframe(df.head())
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elif option == "Train Series":
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df = load_train_series()
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st.subheader("Train Series (1M rows sample)")
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st.dataframe(df.head())
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elif option == "Test Series":
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df = load_test_series()
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st.subheader("Test Series")
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st.dataframe(df.head())
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elif option == "Summary":
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st.subheader("Summary of All Key Datasets")
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with st.expander("📄 Train Events"):
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df_events = load_train_events()
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st.dataframe(df_events.head())
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st.write("Summary:")
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st.dataframe(df_events.describe(include="all"))
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with st.expander("📄 Sample Submission"):
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df_sample = load_sample_submission()
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st.dataframe(df_sample.head())
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st.write("Summary:")
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st.dataframe(df_sample.describe(include="all"))
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with st.expander("📄 Train Series"):
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df_series = load_train_series()
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st.dataframe(df_series.head())
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st.write("Summary:")
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st.dataframe(df_series.describe())
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with st.expander("📄 Test Series"):
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df_test = load_test_series()
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st.dataframe(df_test.head())
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st.write("Summary:")
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st.dataframe(df_test.describe())
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