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import gradio as gr
from huggingface_hub import HfApi
import os
import datasets
from datasets import load_dataset, Dataset
import pandas as pd
TOKEN = os.getenv("TOKEN")
DATASET_ID = os.getenv("DATASET_ID")
datasets.set_caching_enabled(False)
hf_api = HfApi(token=TOKEN)
def create_dataset():
# creates the dataset if it doesn't exist
try:
hf_api.create_repo(repo_id = DATASET_ID, repo_type = "dataset", private=True)
print("created dataset")
initial_dataset()
except:
pass
def initial_dataset():
# creates initial dataset repository and uploads csv file
df = pd.DataFrame(columns=["question", "answer"])
df.loc[len(df)] = ["Find question here", "Find answer here"] # Add first row
# persist initial dataset
df.to_csv("persisted_dataset.csv", index=False)
# push to hub
hf_api.upload_file(path_or_fileobj="persisted_dataset.csv",
path_in_repo = "./persisted_dataset.csv",
repo_id = DATASET_ID, repo_type="dataset")
def write_to_dataset(input):
# if the dataset is not created, submit button will crash to we create it
create_dataset()
# load the dataset, append input and placeholder
dataset = load_dataset(DATASET_ID, data_files="persisted_dataset.csv", use_auth_token=TOKEN)
df = pd.DataFrame(dataset["train"])
df = df.append({'question': input, "answer":"ANSWER HERE"}, ignore_index=True)
df = df.drop_duplicates()
# persist it to local dataset and push it back to Hub
df.to_csv("persisted_dataset.csv", index=False)
hf_api.upload_file(path_or_fileobj="persisted_dataset.csv",
path_in_repo = "./persisted_dataset.csv",
repo_id = DATASET_ID,
repo_type="dataset")
# remove the persisted dataset for privacy
# as of now we don't persist the dataset anyway so let's see if it will stay without removing
# os.remove("persisted_dataset.csv")
return df
def read_dataset():
# read the dataset for second tab, will crash if it doesn't exist
create_dataset()
dataset = load_dataset(DATASET_ID, data_files="persisted_dataset.csv", use_auth_token=TOKEN, download_mode='force_redownload')
# read it to a pandas df
df = pd.DataFrame(dataset["train"])
# return only answered questions
return df[df['answer'] != 'ANSWER HERE']
def render_answers():
# render dataframe into sequentially written text
# used for textbox in second tab
df = read_dataset()
new_df = df.copy()
new_df = new_df.sort_index(ascending=False)
new_df['question'] = '❓ ' + new_df['question']
new_df['answer'] = '🙋 ' + new_df['answer']
str_df = '\n'.join(new_df.apply(lambda x: x['question'] + '\n' + x['answer'] + '\n\n\n', axis=1))
return str_df
with gr.Blocks() as demo:
gr.Markdown("## Ask me anything, I'm not going to lie! 👋 ")
gr.Markdown("In this app, you can write me something anonymous and read my answers to your inputs. ✍️ ")
gr.Markdown("Let's spread love and be respectful 💃🏻🕺🏻")
with gr.Accordion("Open this toggle to see how you can build your own Ask Me Anything app to receive and answer questions ⬇️", open=False):
gr.Markdown("Duplicate this Space by clicking three dots and then `Duplicate this Space` and provide TOKEN and DATASET_ID.")
gr.Markdown("Provide your Hugging Face write token that you can get [here](https://huggingface.co/settings/tokens).")
gr.Markdown("For DATASET_ID, provide something like `merve/answers` it's a dataset repository that will be created through this Space automatically, where your questions will be privately populated.")
gr.Markdown("To answer questions, simply edit the `persisted_dataset.csv` file in the dataset repository, edit `ANSWER HERE` parts beside every question.")
gr.Markdown("The app will not show unanswered questions by default.")
with gr.Tab("Write me something ✍️ "):
input = gr.Textbox(label="What do you want to say to me?")
submit_btn = gr.Button("Submit")
submit_btn.click(fn=write_to_dataset, inputs=[input])
with gr.Tab("Read My Answers"):
answers = render_answers()
gr.Markdown(answers)
refresh_btn = gr.Button("Refresh")
refresh_btn.click(fn=render_answers)
demo.launch(debug=True) |