---
title: Solar_Culient_Predictor
app_file: enhanced_app.py
sdk: gradio
sdk_version: 4.26.0
---
# SOLAI Scoring Dashboard (Gradio)

A lightweight UI to train a baseline logistic regression on your solar leads dataset and generate `probability_to_buy` predictions. Uses the same feature candidates and preprocessing approach as `scripts/batch_scoring.py`.

- Default dataset: `examples/synthetic_v2`
- Outputs are always written to `/Users/git/solai/scores` and are also downloadable from the UI.

## Features

- Choose data source:
  - Use preset example data: `examples/synthetic_v2/leads_features.csv` and `examples/synthetic_v2/outcomes.csv`
  - Upload your own CSVs (features and outcomes)
- Train + score with a single click
- Evaluation metrics (test split):
  - ROC AUC, PR AUC, Brier score (gracefully handles degenerate label cases)
- Preview:
  - `predictions.csv` (lead_id, probability_to_buy)
  - `leads_features_scored.csv` (features merged with probability_to_buy)
- Download both files from the UI in addition to saving to disk (`/Users/git/solai/scores`)

## Requirements

- Python 3.9+ recommended
- macOS (as per environment), should also work on Linux/Windows

Install dependencies (ideally in a virtual environment):

```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r dashboard_gradio/requirements.txt
```

## Run the App

```bash
python dashboard_gradio/app.py
```

Gradio will launch on a local URL (typically http://127.0.0.1:7860). Open it in your browser.

## Usage

1. Start the app.
2. Select a data source:
   - Default: “Use example synthetic_v2”
   - Or switch to “Upload CSVs” and provide:
     - Features CSV (must include `lead_id` and a subset of feature columns listed below)
     - Outcomes CSV (must include `lead_id` and `sold` columns)
3. Click “Train + Score”.
4. Review metrics and preview tables.
5. Download the generated files or find them on disk under `/Users/git/solai/scores`.

## Expected Columns

- Features CSV must contain `lead_id` and some subset of these candidate features:
  - living_area_sqft
  - average_monthly_kwh
  - average_monthly_bill_usd
  - shading_factor
  - roof_suitability_score
  - seasonality_index
  - electric_panel_amperage
  - has_pool
  - is_remote_worker_household
  - tdsp
  - rate_structure
  - credit_score_range
  - household_income_bracket
  - preferred_financing_type
  - neighborhood_type

- Outcomes CSV must contain:
  - `lead_id`
  - `sold` (0/1)

## Outputs

Saved to `/Users/git/solai/scores` with a timestamp suffix:

- `predictions_YYYYMMDD_HHMMSS.csv`
  - Columns: `lead_id`, `probability_to_buy`
- `leads_features_scored_YYYYMMDD_HHMMSS.csv`
  - Original features merged with `probability_to_buy`

Both files are also offered as downloads directly in the UI.

## Notes and Troubleshooting

- If the outcomes data has only a single class (all sold=0 or all sold=1), ROC AUC and PR AUC are undefined; the app shows “N/A” for those metrics but still computes Brier score and produces predictions.
- If you see “No candidate features found”, ensure your features CSV contains at least one of the listed feature names.
- If port 7860 is in use, Gradio will choose another port automatically, displayed in the terminal.
- For larger datasets, training time may increase but should remain quick for typical CSV sizes.

## Development

- Core logic is in `dashboard_gradio/app.py`.
- The pipeline mirrors `scripts/batch_scoring.py`: ColumnTransformer with passthrough numeric features and OneHotEncoder for categoricals, then LogisticRegression.
- Extend easily with additional visualizations (e.g., calibration plots), feature importance, or a data dictionary viewer.