Update README with setup instructions for BigQuery and Streamlit

README.md

@@ -24,13 +24,66 @@ This project analyzes MTA Daily Ridership Data to examine COVID-19 recovery patt
 
 ## Setup
 
-1. Clone this repository: `git clone https://github.com/advanced-computing/bouncing-penguin.git`
-2. Create virtual environment: `python -m venv .venv`
-3. Activate virtual environment:
-   - Mac/Linux: `source .venv/bin/activate`
-   - Windows: `.venv\Scripts\activate`
-4. Install dependencies: `pip install -r requirements.txt`
+### 1. Clone and install
+
+```bash
+git clone https://github.com/advanced-computing/bouncing-penguin.git
+cd bouncing-penguin
+python -m venv .venv
+source .venv/bin/activate  # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+```
+
+### 2. Configure BigQuery credentials
+
+The app reads MTA data from BigQuery. You need a service account key to connect.
+
+1. Get the service account key JSON for `streamlit@sipa-adv-c-bouncing-penguin.iam.gserviceaccount.com` from your team or GCP Console (IAM & Admin > Service Accounts > Keys).
+2. Create the secrets file:
+
+```bash
+mkdir -p .streamlit
+```
+
+3. Create `.streamlit/secrets.toml` with the following structure, filling in values from the JSON key:
+
+```toml
+[gcp_service_account]
+type = "service_account"
+project_id = "sipa-adv-c-bouncing-penguin"
+private_key_id = "<from JSON>"
+client_email = "streamlit@sipa-adv-c-bouncing-penguin.iam.gserviceaccount.com"
+client_id = "<from JSON>"
+auth_uri = "https://accounts.google.com/o/oauth2/auth"
+token_uri = "https://oauth2.googleapis.com/token"
+private_key = "<from JSON>"
+```
+
+### 3. Load data into BigQuery (optional)
+
+If the BigQuery table doesn't exist yet, run the data loading script:
+
+```bash
+python load_data_to_bq.py
+```
+
+This fetches MTA ridership data from the NYC Open Data API and uploads it to BigQuery. You will be prompted to authenticate with your Google account.
+
+### 4. Run the app
+
+```bash
+streamlit run streamlit_app.py
+```
+
+The app will open at `http://localhost:8501`.
+
+## Live App
+
+[bouncing-penguin-forever.streamlit.app](https://bouncing-penguin-forever.streamlit.app)
 
 ## Usage
 
-Open `mta_ridership_project.ipynb` in Jupyter Notebook or VS Code to run the analysis.
+- **Dashboard tab**: Interactive visualizations of MTA ridership recovery trends, weekday vs weekend comparisons, holiday impacts, and year-over-year analysis.
+- **Proposal tab**: Project background, research questions, methodology, and preliminary findings.
+- **MTA Ridership page**: Simplified ridership charts.
+- **Second Dataset page**: NYC COVID-19 case data for context.