How darshitn04-blip/market-size-in works
MarketSize.in is a Streamlit decision-support dashboard for ranking Indian Tier-2 cities for quick-commerce expansion. Users adjust market assumptions and may upload verified competitor counts; the app runs a model that estimates bottom-up and top-down TAM, scores and ranks cities, and clusters opportunity profiles. Results appear as recommendations, sizing views, and a map, and are exported to CSV and a regenerated SQLite warehouse. The README documents these capabilities; the shown source supports the app-to-model and model-to-output links.
Main entry points: app.py Streamlit App · analytics.py Model Engine
Components 13
Dashboard
- Streamlit App
app.py
Market Model
- City Baseline
analytics.py - Model Engine
analytics.py - TAM Estimates
analytics.py - Attractiveness Ranking
analytics.py - City Clusters
analytics.py
Decision Outputs
- City Recommendation
app.py - Market Sizing Views
app.py - Candidate Map
app.py - Scores CSV
app.py - Warehouse Writer
analytics.py - SQLite Warehouse
Other components
- Decision Maker
Connections 13
- Decision Maker → Streamlit App: sets assumptions
app.py - Decision Maker → Streamlit App: uploads counts
app.py - Streamlit App → City Baseline: loads cities
app.py - Streamlit App → Model Engine: runs model
app.py - Model Engine → TAM Estimates: calculates TAM
analytics.py - Model Engine → Attractiveness Ranking: scores cities
analytics.py - Model Engine → City Clusters: clusters profiles
analytics.py - Streamlit App → City Recommendation: renders results
app.py - Streamlit App → Market Sizing Views: renders results
app.py - Streamlit App → Candidate Map: renders scores
app.py - Streamlit App → Scores CSV: exports scores
app.py - Streamlit App → Warehouse Writer: saves model
app.py - Warehouse Writer → SQLite Warehouse: writes tables
analytics.py