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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) |