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import functions as funky
import pandas as pd
import gradio as gr
import numpy as np
from datetime import datetime
import os
from datasets import load_dataset
from huggingface_hub import login
login(token = os.environ['HUB_TOKEN'])
dataset = load_dataset("bradley6597/illustration-test")
df = pd.DataFrame(dataset['train'])
logger = gr.HuggingFaceDatasetSaver(os.environ['HUB_TOKEN'], dataset_name='illustration_gdrive_logging_main', organization=None, private=True)
logger.setup([gr.Text(label="clicked_url"), gr.Text(label="seach_term"), gr.Text(label = 'sessionhash'), gr.Text(label = 'datetime')], './flagged_data_points')
logging_js = '''
function magicFunc(x){
let script = document.createElement('script');
script.innerHTML = "async function magicFunc(x){let z = document.getElementById('search_term').getElementsByTagName('textarea')[0].value; let data = {data: [x, z]}; var x = fetch('/api/track', {method: 'POST', headers: {'Content-Type': 'application/json'}, body: JSON.stringify(data)})}";
document.head.appendChild(script);
}
'''
ill_links = df
ill_links = ill_links[ill_links['Description'] != 'Moved'].copy()
ill_links['code'] = ill_links['link'].str.replace("https://drive.google.com/file/d/", "", regex = False)
ill_links['code'] = ill_links['code'].str.replace("/view?usp=drivesdk", "", regex = False)
# ill_links['image_code'] = 'https://lh3.google.com/u/0/d/' + ill_links['code'] + '=k'
ill_links['image_code'] = 'https://lh3.google.com/u/0/d/' + ill_links['code'] + '=w320-h304'
ill_links['image_code'] = '<center><a href="' + ill_links['link'] + '" target="_blank" onclick="magicFunc(\'' + ill_links['code'] + '\')"><img src="' + ill_links['image_code'] + '" style="max-height:400px; max-width:200px"></a></center>'
ill_links['filename'] = ill_links['file'].str.replace(".*\\/", "", regex = True)
ill_links['shared_drive'] = ill_links['file'].str.replace("/content/drive/Shareddrives/", "", regex = False)
ill_links['shared_drive'] = ill_links['shared_drive'].str.replace("(.*?)\\/.*", "\\1", regex = True)
ill_links['Description'] = ill_links['Description'].str.replace("No Description", "", regex = False)
ill_links['image_code'].iloc[0]
ill_links_title = ill_links.copy()
ill_links['ID'] = ill_links.index
ill_links_title['ID'] = ill_links_title.index
ill_links['title'] = ill_links['filename']
ill_links_title['title'] = ill_links_title['filename']
ill_links['url'] = ill_links['image_code']
ill_links_title['url'] = ill_links_title['image_code']
ill_links['abstract'] = ill_links['filename'].str.replace("\\-|\\_", " ", regex = True) + ' ' + ill_links['Description'].str.replace(",", " ", regex = False).astype(str)
ill_links_title['abstract'] = ill_links_title['filename'].str.replace('\\-|\\_', " ", regex = True)
ill_links['filepath'] = ill_links['file']
ill_links_title['filepath'] = ill_links_title['file']
ill_links['post_filepath'] = ill_links['filepath'].str.replace(".*?\\/KS1 EYFS\\/", "", regex = True)
ill_links_title['post_filepath'] = ill_links_title['filepath'].str.replace(".*?\\/KS1 EYFS\\/", "", regex = True)
ill_links = ill_links[['ID', 'title', 'url', 'abstract', 'filepath', 'Date Created', 'post_filepath']]
ill_links_title = ill_links_title[['ID', 'title', 'url', 'abstract', 'filepath', 'Date Created', 'Description', 'post_filepath']]
ill_check_lst = []
for i in range(0, 5):
tmp_links = ill_links['url'].iloc[0].replace("/u/0/", f"/u/{i}/")
tmp_links = tmp_links.replace('max-width:200px', 'max-width:25%')
tmp_links = tmp_links.replace("<center>", "")
tmp_links = tmp_links.replace("</center>", "")
tmp_links = f'<p>{i}</p>' + tmp_links
ill_check_lst.append(tmp_links)
ill_check_df = pd.DataFrame(ill_check_lst).T
ill_check_html = ill_check_df.to_html(escape = False, render_links = True, index = False, header = False)
ind_main, doc_main, tf_main = funky.index_documents(ill_links)
ind_title, doc_title, tf_title = funky.index_documents(ill_links_title)
def same_auth(username, password):
return(username == os.environ['username']) & (password == os.environ['password'])
def search_index(search_text, sd, ks, sort_by, max_results, user_num, search_title):
if search_title:
output = funky.search(tf_title, doc_title, ind_title, search_text, search_type = 'AND', ranking = True)
else:
output = funky.search(tf_main, doc_main, ind_main, search_text, search_type='AND', ranking = True)
output = [x for o in output for x in o if type(x) is not float]
output_df = pd.DataFrame(output).reset_index(drop = True)
if output_df.shape[0] > 0:
output_df['url'] = output_df['url'].str.replace("/u/0/", f"/u/{int(user_num)}/", regex = False)
if len(sd) == 1:
output_df = output_df[(output_df['filepath'].str.contains(str(sd[0]), regex = False))]
if len(ks) > 0:
keystage_filter = '|'.join(ks).lower()
if search_title:
output_df['abstract'] = output_df['abstract'] + ' ' + output_df['Description']
output_df['abstract'] = output_df['abstract'].str.lower()
output_df['post_filepath'] = output_df['post_filepath'].str.lower()
output_df['missing_desc'] = np.where(output_df['abstract'].str.contains('eyfs|ks1|ks2', regex = True), 0, 1)
output_df2 = output_df[(output_df['abstract'].str.contains(keystage_filter, regex = True) | (output_df['missing_desc'] == 1))].copy()
output_df2 = output_df2[(output_df2['post_filepath'].str.contains(keystage_filter, regex = True))]
if output_df2.shape[0] == 0:
output_df2 = output_df[(output_df['post_filepath'].str.contains(keystage_filter, regex = True))]
output_df2['ind'] = output_df2.index
if sort_by == 'Relevance':
output_df2 = output_df2.sort_values(by = ['missing_desc', 'ind'], ascending = [True, True])
elif sort_by == 'Date Created':
output_df2 = output_df2.sort_values(by = ['Date Created'], ascending = False)
elif sort_by == 'A-Z':
output_df2 = output_df2.sort_values(by = ['title'], ascending = True)
output_df2 = output_df2.head(int(max_results))
output_df2 = output_df2[['url']].reset_index(drop = True)
max_cols = 5
output_df2['row'] = output_df2.index % max_cols
for x in range(0, max_cols):
tmp = output_df2[output_df2['row'] == x].reset_index(drop = True)
tmp = tmp[['url']]
if x == 0:
final_df = tmp
else:
final_df = pd.concat([final_df, tmp], axis = 1)
final_df = final_df.fillna('')
else:
final_df = pd.DataFrame(['<h3>No Results Found :(</h3>'])
if final_df.shape[0] == 0 :
final_df = pd.DataFrame(['<h3>No Results Found :(</h3>'])
return('<center>' +
final_df.to_html(escape = False, render_links = True, index = False, header = False) +
'</center>')
def search_logging(url: str, q: str, request: gr.Request):
if url == q:
url = ''
session_id = getattr(request.cookies, 'access-token')
logger.flag([url, q, session_id, str(datetime.now())])
with gr.Blocks(css="style.css") as app:
with gr.Row():
with gr.Column(min_width = 10):
with gr.Row():
gr.HTML("<center><p>If you can't see the images please make sure you are signed in to your Twinkl account on Google & you have access to the Shared Drives you are searching :)</p></center>")
gr.HTML(ill_check_html)
user_num = gr.Number(value = 0, label = 'Put lowest number of the alarm clock you can see')
with gr.Row():
search_prompt = gr.Textbox(placeholder = 'search for an illustration', label = 'Search', elem_id = 'search_term')
title_search = gr.Checkbox(label = 'Search title only')
# with gr.Row():
shared_drive = gr.Dropdown(choices = ['Illustrations - 01-10 to 07-22', 'Illustrations - Now'], multiselect = True, label = 'Shared Drive', value = ['Illustrations - 01-10 to 07-22', 'Illustrations - Now'])
key_stage = gr.Dropdown(choices = ['EYFS', 'KS1', 'KS2'], multiselect = True, label = 'Key Stage', value = ['EYFS', 'KS1', 'KS2'])
sort_by = gr.Dropdown(choices = ['Relevance', 'Date Created', 'A-Z'], value = 'Relevance', multiselect = False, label = 'Sort By')
max_return = gr.Dropdown(choices = ['10', '25', '50', '75', '100', '250', '500'], value = '10', multiselect = False, label = 'No. of Results to Return')
with gr.Row():
search_button = gr.Button(value="Search!")
with gr.Row():
output_df = gr.HTML()
search_button.click(search_index, inputs=[search_prompt, shared_drive, key_stage, sort_by, max_return, user_num, title_search], outputs=output_df)
search_prompt.submit(search_index, inputs=[search_prompt, shared_drive, key_stage, sort_by, max_return, user_num, title_search], outputs=output_df)
search_button.click(search_logging, inputs=[search_prompt, search_prompt], outputs=None, api_name='track')
search_prompt.submit(search_logging, inputs=[search_prompt, search_prompt], outputs=None, api_name='track')
app.load(_js = logging_js)
app.auth = (same_auth)
app.auth_message = ''
app.launch(debug=True,
share=False,
height=768,
auth=same_auth
)
# app.close()
# from fastapi import FastAPI, Request
# import uvicorn
# from starlette.middleware.sessions import SessionMiddleware
# fapi = FastAPI()
# fapi.add_middleware(SessionMiddleware, secret_key=os.environ['session_key'])
# @fapi.middleware("http")
# async def add_session_hash(request: Request, call_next):
# response = await call_next(request)
# session = request.cookies.get('session')
# if session:
# response.set_cookie(key='session', value=request.cookies.get('session'), httponly=True)
# return response
# # custom get request handler with params to flag clicks
# @ fapi.get("/track")
# async def track(url: str, q: str, request: Request):
# print(request)
# if q is None:
# q = ''
# logger.flag([url, q, request.cookies['access-token'], str(datetime.now())])
# return {"message": "ok"}
# logger.flag(['test', 'test', 'test', str(datetime.now())])
# # mount Gradio app to FastAPI app
# app2 = gr.mount_gradio_app(fapi, app, path="/")
# # serve the app
# if __name__ == "__main__":
# uvicorn.run(app2, host="0.0.0.0", port=7860) |