app.py

import gradio as gr
from pyterrier_doc2query import Doc2Query

doc2query = Doc2Query()

def df2code(df):
  rows = []
  for row in df.itertuples(index=False):
    rows.append(f'  {dict(row._asdict())},')
  rows = '\n'.join(rows)
  return f'''pd.DataFrame([
{rows}
])'''

def predict(input, model, append, num_samples):
  assert model == 'macavaney/doc2query-t5-base-msmarco'
  doc2query.append = append
  doc2query.num_samples = num_samples
  code = f'''
**Code:**

```python
import pandas as pd
from pyterrier_doc2query import Doc2Query
doc2query = Doc2Query({repr(model)}, append={append}, num_samples={num_samples})
doc2query({df2code(input)})
```
'''
  return (doc2query(input), code)

gr.Interface(
    predict,
    inputs=[gr.Dataframe(
      headers=["docno", "text"],
      datatype=["str", "str"],
      col_count=(2, "fixed"),
      row_count=1,
      wrap=True,
      label='Pipeline Input',
      value=[['0', 'The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.']],
    ), gr.Dropdown(
      choices=['macavaney/doc2query-t5-base-msmarco'],
      value='macavaney/doc2query-t5-base-msmarco',
      label='Model',
      interactive=False,
    ), gr.Checkbox(
      value=False,
      label="Append",
    ), gr.Slider(
      minimum=1,
      maximum=10,
      value=3,
      step=1.,
      label='# Queries'
    )],
    outputs=[gr.Dataframe(
      headers=["docno", "text", "querygen"],
      datatype=["str", "str", "str"],
      col_count=(3, "fixed"),
      row_count=1,
      wrap=True,
      label='Pipeline Output',
      value=[["[docno]", "[text]", "[querygen]"]],
    ), gr.Markdown()],
    title="PyTerrier: Doc2Query",
    description=open('README.md', 'rt').read().split('---\n')[-1],
    allow_flagging='never',
).launch()