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src/streamlit_app.py

@@ -1,40 +1,111 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import numpy as np
+
+# Page config
+st.set_page_config(
+    page_title="Uber NYC Pickups",
+    page_icon="🚕",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Header with required link
+st.title("🚕 Uber NYC Pickups")
+st.markdown("[Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)")
+
+# Load data (simulate as if from the repo; in practice, download or use cached)
+@st.cache_data
+def load_data():
+    # For demo purposes, generate sample data similar to uber-raw-data-sep14.csv
+    # In a real app, load from 'https://github.com/streamlit/demo-uber-nyc-pickups/raw/master/uber-raw-data-sep14.csv'
+    np.random.seed(42)
+    n_points = 10000
+    dates = pd.date_range('2014-09-01', periods=n_points, freq='min')
+    lats = np.random.normal(40.75, 0.1, n_points)
+    lons = np.random.normal(-73.97, 0.1, n_points)
+    df = pd.DataFrame({
+        'Date/Time': dates,
+        'Lat': lats,
+        'Lon': lons,
+        'Base': np.random.choice(['B02512', 'B02598'], n_points)
+    })
+    return df
+
+df = load_data()
+
+# Sidebar filters
+st.sidebar.header("Filters")
+date_range = st.sidebar.slider(
+    "Select Date Range",
+    min_value=df['Date/Time'].min(),
+    max_value=df['Date/Time'].max(),
+    value=(df['Date/Time'].min(), df['Date/Time'].max()),
+    format="YYYY-MM-DD HH:MM"
+)
+hour_filter = st.sidebar.slider("Select Hour of Day", 0, 23, (0, 23))
+
+base_filter = st.sidebar.multiselect(
+    "Select Base",
+    options=df['Base'].unique(),
+    default=df['Base'].unique()
+)
+
+# Filter data
+filtered_df = df[
+    (df['Date/Time'] >= date_range[0]) &
+    (df['Date/Time'] <= date_range[1]) &
+    (df['Date/Time'].dt.hour.between(hour_filter[0], hour_filter[1])) &
+    (df['Base'].isin(base_filter))
+].copy()
+
+# Metrics
+col1, col2, col3 = st.columns(3)
+with col1:
+    st.metric("Total Pickups", len(filtered_df))
+with col2:
+    avg_hour = filtered_df['Date/Time'].dt.hour.mean()
+    st.metric("Average Hour", f"{avg_hour:.0f}")
+with col3:
+    unique_bases = filtered_df['Base'].nunique()
+    st.metric("Unique Bases", unique_bases)
+
+# Visualizations
+if len(filtered_df) > 0:
+    # Map
+    st.subheader("Pickups Map")
+    fig_map = px.scatter_mapbox(
+        filtered_df,
+        lat="Lat",
+        lon="Lon",
+        color="Base",
+        hover_data=["Date/Time"],
+        zoom=10,
+        height=500,
+        mapbox_style="carto-positron"
+    )
+    st.plotly_chart(fig_map, use_container_width=True)
+
+    # Time series
+    st.subheader("Pickups Over Time")
+    filtered_df['Date'] = filtered_df['Date/Time'].dt.date
+    hourly_data = filtered_df.groupby(filtered_df['Date/Time'].dt.hour).size().reset_index(name='Count')
+    fig_time = px.line(hourly_data, x='Date/Time', y='Count', title="Pickups by Hour")
+    st.plotly_chart(fig_time, use_container_width=True)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # Hourly heatmap
+    st.subheader("Hourly Distribution")
+    hourly_dist = filtered_df.groupby('Date/Time').size().reset_index(name='Count')
+    fig_heatmap = px.density_heatmap(
+        filtered_df,
+        x=filtered_df['Date/Time'].dt.hour,
+        y=filtered_df['Date/Time'].dt.date,
+        z=filtered_df.groupby([filtered_df['Date/Time'].dt.date, filtered_df['Date/Time'].dt.hour]).size().values.reshape(-1, 24),
+        color_continuous_scale="Viridis"
+    )
+    st.plotly_chart(fig_heatmap, use_container_width=True)
+else:
+    st.info("No data matches the selected filters.")