| --- |
| title: MorphSQL |
| emoji: 🧬 |
| colorFrom: blue |
| colorTo: green |
| sdk: gradio |
| sdk_version: "5.49.1" |
| python_version: "3.11" |
| app_file: app.py |
| pinned: true |
| license: apache-2.0 |
| short_description: Convert SQL to pandas, PySpark, warehouses, or dbt |
| tags: |
| - sql |
| - pandas |
| - pyspark |
| - code |
| - data-science |
| - machine-learning |
| - feature-extraction |
| - snowflake |
| - dbt |
| - data-engineering |
| models: |
| - dgvj-work/morphsql |
| datasets: |
| - dgvj-work/vertica-snowflake-pairs |
| suggested_hardware: cpu-basic |
| --- |
| |
| # MorphSQL |
|
|
| Convert warehouse SQL → **pandas**, **PySpark**, Snowflake, BigQuery, or **dbt** — then **download** the result. |
|
|
| Package / CLI / Hub: `morphsql` · Space: [dgvj-work/morphsql](https://huggingface.co/spaces/dgvj-work/morphsql) |
|
|
| ## How to use (30 seconds) |
|
|
| 1. Open the **Convert** tab |
| 2. Pick source dialect + **Convert to** target |
| 3. Load an example, paste SQL, or **upload** a `.sql` / `.zip` |
| 4. Click **Convert** or **Upload & Convert → Download** |
| 5. Download the `.py` / `.sql` / `.zip` to your machine |
|
|
| | Target | Download | |
| |--------|----------| |
| | pandas / PySpark | `.py` ready for notebooks / Databricks | |
| | Snowflake / BigQuery | `.sql` | |
| | dbt | `.txt` project preview (or zip for batches) | |
|
|
| ## Python API |
|
|
| ```python |
| from morphsql.ai import pipeline |
| |
| out = pipeline("sql-migration")( |
| "SELECT COALESCE(a, 0) FROM t", |
| source="snowflake", |
| target="pandas", # or "pyspark", "snowflake", "bigquery", "dbt-snowflake" |
| ) |
| print(out["converted_sql"][:500]) |
| ``` |
|
|
| ## More tab |
|
|
| Object risk assess · repo workbench · ML feature SQL · copilot · dialect notes · offline eval |
|
|
| Author: Digvijay Waghela · Apache-2.0 · [GitHub](https://github.com/dgvj-work/morphsql) |
|
|
