Upload streamlit_taxi_app.py

streamlit_taxi_app.py

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+# Streamlit App: NYC Taxi Anomaly Detector with Event Markers
+
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+st.set_page_config(page_title="NYC Taxi Anomaly Detector", layout="wide")
+st.title("🚕 NYC Taxi Passenger Count - Anomaly Detection")
+
+# Load data
+@st.cache_data
+def load_data():
+    df = pd.read_csv('nyc_taxi_real_anomalies.csv', parse_dates=['tpep_pickup_datetime'], index_col='tpep_pickup_datetime')
+    return df
+
+df = load_data()
+
+# Define NYC events/holidays
+events = {
+    "New Year\'s Eve": "2015-12-31",
+    "New Year\'s Day": "2016-01-01",
+    "Martin Luther King Jr. Day": "2016-01-18"
+}
+
+# Sidebar controls
+st.sidebar.header("Filters")
+start_date = st.sidebar.date_input("Start Date", df.index.min().date())
+end_date = st.sidebar.date_input("End Date", df.index.max().date())
+threshold_slider = st.sidebar.slider("Anomaly Threshold (%)", 90, 99, 95)
+
+# Filtered data
+filtered_df = df.loc[str(start_date):str(end_date)]
+
+# Apply new threshold
+new_threshold = filtered_df['reconstruction_error'].quantile(threshold_slider / 100.0)
+filtered_df['anomaly_custom'] = filtered_df['reconstruction_error'] > new_threshold
+
+# Plot
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(filtered_df.index, filtered_df['passenger_count'], label='Passenger Count')
+ax.scatter(filtered_df[filtered_df['anomaly_custom']].index,
+           filtered_df[filtered_df['anomaly_custom']]['passenger_count'],
+           color='red', label='Anomaly')
+
+# Add event markers
+for name, date_str in events.items():
+    event_date = pd.to_datetime(date_str)
+    if event_date in filtered_df.index:
+        ax.axvline(event_date, color='orange', linestyle='--', alpha=0.7)
+        ax.text(event_date, ax.get_ylim()[1]*0.9, name, rotation=90, color='orange', fontsize=8)
+
+ax.set_title('Anomaly Detection with NYC Event Markers')
+ax.legend()
+ax.set_xlabel("Date")
+ax.set_ylabel("Passenger Count")
+plt.xticks(rotation=45)
+st.pyplot(fig)
+
+# Show data table
+with st.expander("📄 View Data Table"):
+    st.dataframe(filtered_df[['passenger_count', 'reconstruction_error', 'anomaly_custom']])
+
+# Download
+st.download_button("Download Anomalies CSV", data=filtered_df.to_csv().encode('utf-8'), file_name="filtered_anomalies.csv", mime="text/csv")