Add @st.cache_data to all builders, .gitignore, and live-demo badge

[Copilot]

The app had no caching — every user visit and every widget interaction re-read the CSV and rebuilt all 8 charts from scratch. First-deploy load time was also a concern.

Changes

• dashboard.py — added @st.cache_data to all 9 functions (load_data, every chart builder, and build_route_map). Results are computed once per cold start and served from cache on all subsequent interactions.

# Before
def load_data(csv_path: str = "SmartTourRoutePlanner.csv") -> pd.DataFrame:
    return pd.read_csv(csv_path)

# After
@st.cache_data
def load_data(csv_path: str = "SmartTourRoutePlanner.csv") -> pd.DataFrame:
    return pd.read_csv(csv_path)

• README.md — added Streamlit badge and live-demo URL at the top so visitors can reach the deployed app directly.

• .gitignore — added (was missing); excludes __pycache__/, *.pyc, .ipynb_checkpoints/, and common OS artifacts.

Original prompt

Goal

Replace the existing Dash-based dashboard.py with a Streamlit-based dashboard that includes all 7 existing Plotly charts plus the Folium geographical map from the notebook. Add deployment config for Streamlit Community Cloud.

Existing Files

• SmartTourRoutePlan.ipynb — the original notebook
• SmartTourRoutePlanner.csv — the dataset (500 rows, 19 columns)
• dashboard.py — current Dash-based dashboard with 7 Plotly charts
• travel_route_map.html — the Folium map output from the notebook

What to do

1. Rewrite dashboard.py using Streamlit

Convert the existing Dash app to Streamlit. Keep ALL 7 existing chart functions exactly as they are (same Plotly code, same titles, same colors, same parameters):

• build_traffic_time_line — Line plot: Impact of Traffic Density on Estimated Travel Time
• build_demand_heatmap — Density heatmap: Travel Demand (Day Type vs Season) with transport animation
• build_budget_satisfaction_scatter — Scatter: User Budget vs Satisfaction Rating by Transport Mode
• build_cost_sankey — Sankey: Travel Cost Flow Distribution
• build_travel_preference_sunburst — Sunburst: Season → Transport Mode → Destination Type
• build_transport_radar — Radar chart comparing transport modes across travel factors
• build_traffic_violin — Violin: Traffic density distribution by day_type

Replace Dash layout (dcc.Tabs, dcc.Graph, html.Div) with Streamlit equivalents (st.tabs, st.plotly_chart, st.title, etc.).

2. Add the Folium Map Tab — EXACT code from notebook

Add a new tab called "🗺️ Route Map" with the exact Folium map from the notebook. Use streamlit-folium (st_folium) to embed it. Here is the exact map code to use:

import folium
from streamlit_folium import st_folium

city_coords = {
    "Delhi": (28.6139, 77.2090),
    "Mumbai": (19.0760, 72.8777),
    "Bangalore": (12.9716, 77.5946),
    "Chennai": (13.0827, 80.2707),
    "Kolkata": (22.5726, 88.3639),
    "Agra": (27.1767, 78.0081),
    "Goa": (15.2993, 74.1240),
    "Shimla": (31.1048, 77.1734),
    "Ooty": (11.4064, 76.6932),
    "Mahabalipuram": (12.6208, 80.1937)
}

df["start_lat"]=df["start_location"].map(lambda x: city_coords.get(x, (None, None))[0])
df["start_lon"]=df["start_location"].map(lambda x: city_coords.get(x, (None, None))[1])

df["end_lat"]=df["end_location"].map(lambda x: city_coords.get(x, (None, None))[0])
df["end_lon"]=df["end_location"].map(lambda x: city_coords.get(x, (None, None))[1])

m=folium.Map(location=[22.5, 80], zoom_start=5, tiles="cartodbpositron")

mode_colors={
    "car":"blue",
    "train":"green",
    "bike":"orange",
    "walk":"purple",
    "bus":"brown"
}

for _, row in df.iterrows():
    if pd.notnull(row["start_lat"]) and pd.notnull(row["end_lat"]):
        start=(row["start_lat"], row["start_lon"])
        end=(row["end_lat"], row["end_lon"])
        color=mode_colors.get(str(row["transport_mode"]).lower(), "black")

        folium.PolyLine(
            locations=[start, end],
            color=color,
            weight=3,
            opacity=0.8,
            tooltip=f"{row['start_location']} → {row['end_location']} ({row['transport_mode']})"
        ).add_to(m)

        folium.CircleMarker(
            location=start,
            radius=row["popularity_score"] / 20,
            color="blue",
            fill=True,
            fill_opacity=0.7,
            popup=f"Start: {row['start_location']}"
        ).add_to(m)

        folium.CircleMarker(
            location=end,
            radius=row["popularity_score"] / 20,
            color="orange",
            fill=True,
            fill_opacity=0.7,
            popup=f"End: {row['end_location']}"
        ).add_to(m)

Then render with: st_folium(m, width=900, height=600)

3. Add requirements.txt

streamlit
pandas
plotly
scikit-learn
folium
streamlit-folium

4. Add .streamlit/config.toml

[theme]
primaryColor = "#4C78A8"
backgroundColor = "#FFFFFF"
secondaryBackgroundColor = "#F0F2F6"
textColor = "#262730"

[server]
headless = true
port = 8501

5. Update README.md

Add sections for:

• Project description
• How to run locally (pip install -r requirements.txt then streamlit run dashboard.py)
• How to deploy on Streamlit Community Cloud
• Description of all dashboard tabs including the map

Important

• Do NOT delete or change the notebook or CSV file
• The travel_route_map.html can remain as-is (it's a static export)
• ALL 7 Plotly chart functions must produce identical output — same data processing, same colors, same titles, same layout configs
• The Folium map must be an EXACT replica of the notebook code shown above
• Use st.plotly_chart(fig, use_container_width=True) for all Plotly charts

The following is the prior conversation context from the user's chat exploration (may be truncated):

Assistant: Both projects are live! 🎉 I've analyzed everything. Here's what I found:

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[budugameghraj]

streamlit