Upload app.py

app.py

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