app.py

import pandas as pd
import gradio as gr
from pyterrier_doc2query import Doc2Query
from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md, EX_D

MODEL = 'macavaney/doc2query-t5-base-msmarco'

doc2query = Doc2Query(MODEL, append=True, num_samples=5)

COLAB_NAME = 'pyterrier_doc2query.ipynb'
COLAB_INSTALL = '''
!pip install -q git+https://github.com/terrier-org/pyterrier
!pip install -q git+https://github.com/terrierteam/pyterrier_doc2query
'''.strip()

def predict(input, model, append, num_samples):
  assert model == MODEL
  doc2query.append = append
  doc2query.num_samples = num_samples
  code = f'''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), code2md(code, COLAB_INSTALL, COLAB_NAME))

interface(
  MarkdownFile('README.md'),
  Demo(
    predict,
    EX_D,
    [
    gr.Dropdown(
      choices=[MODEL],
      value=MODEL,
      label='Model',
      interactive=False,
    ), gr.Checkbox(
      value=doc2query.append,
      label="Append",
    ), gr.Slider(
      minimum=1,
      maximum=10,
      value=doc2query.num_samples,
      step=1.,
      label='# Queries'
    )],
  ),
  MarkdownFile('wrapup.md'),
).launch(share=False)