import re
import json
import pandas as pd
import gradio as gr
import pyterrier as pt
pt.init()
import pyt_splade
from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md
factory_max = pyt_splade.SpladeFactory(agg='max')
factory_sum = pyt_splade.SpladeFactory(agg='sum')

COLAB_NAME = 'pyterrier_splade.ipynb'
COLAB_INSTALL = '''
!pip install -q git+https://github.com/naver/splade
!pip install -q git+https://github.com/seanmacavaney/pyt_splade@misc
'''.strip()

def generate_vis(df, mode='Document'):
  if len(df) == 0:
    return ''
  result = []
  if mode == 'Document':
    max_score = max(max(t.values()) for t in df['toks'])
  for row in df.itertuples(index=False):
    if mode == 'Query':
      tok_scores = {m.group(2): float(m.group(1)) for m in re.finditer(r'combine:0=([0-9.]+)\(([^)]+)\)', row.query)}
      max_score = max(tok_scores.values())
      orig_tokens = factory_max.tokenizer.tokenize(row.query_0)
      id = row.qid
    else:
      tok_scores = row.toks
      orig_tokens = factory_max.tokenizer.tokenize(row.text)
      id = row.docno
    def toks2span(toks):
      return '<kbd> </kbd>'.join(f'<kbd style="background-color: rgba(66, 135, 245, {tok_scores.get(t, 0)/max_score});">{t}</kbd>' for t in toks)
    orig_tokens_set = set(orig_tokens)
    exp_tokens = [t for t, v in sorted(tok_scores.items(), key=lambda x: (-x[1], x[0])) if t not in orig_tokens_set]
    result.append(f'''
<div style="font-size: 1.2em;">{mode}: <strong>{id}</strong></div>
<div style="margin: 4px 0 16px; padding: 4px; border: 1px solid black;">
<div>
{toks2span(orig_tokens)}
</div>
<div><strong>Expansion Tokens:</strong> {toks2span(exp_tokens)}</div>
</div>
''')
  return '\n'.join(result)

def predict_query(input, agg):
  code = f'''import pandas as pd
import pyterrier as pt ; pt.init()
import pyt_splade

factory = pyt_splade.SpladeFactory(agg={agg})

query_pipeline = factory.query()

query_pipeline({df2code(input)})
'''
  pipeline = {
    'max': factory_max,
    'sum': factory_sum
  }[agg].query()
  res = pipeline(input)
  vis = generate_vis(res, mode='Query')
  return (res, code2md(code, COLAB_INSTALL, COLAB_NAME), vis)

def predict_doc(input, agg):
  code = f'''import pandas as pd
import pyterrier as pt ; pt.init()
import pyt_splade

factory = pyt_splade.SpladeFactory(agg={agg})

doc_pipeline = factory.indexing()

doc_pipeline({df2code(input)})
'''
  pipeline = {
    'max': factory_max,
    'sum': factory_sum
  }[agg].indexing()
  res = pipeline(input)
  vis = generate_vis(res, mode='Document')
  res['toks'] = [json.dumps({k: round(v, 4) for k, v in t.items()}) for t in res['toks']]
  return (res, code2md(code, COLAB_INSTALL, COLAB_NAME), vis)

interface(
  MarkdownFile('README.md'),
  MarkdownFile('query.md'),
  Demo(
    predict_query,
    pd.DataFrame([
      {'qid': '1112389', 'query': 'what is the county for grand rapids, mn'},
    ]),
    [
      gr.Dropdown(choices=['max', 'sum'], value='max', label='Aggregation'),
    ],
    scale=2/3
  ),
  MarkdownFile('doc.md'),
  Demo(
    predict_doc,
    pd.DataFrame([
      {'docno': '0', 'text': '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=['max', 'sum'], value='max', label='Aggregation'),
    ],
    scale=2/3
  ),
  MarkdownFile('wrapup.md'),
).launch(share=False)