Hugging Face's logo

Spaces:
trl-internal-testing
/
rlhf_dialog_experiment
Runtime error

App Files Community
rlhf_dialog_experiment
File size: 4,310 Bytes 
cb5c850
 
 
 
 
 
 
 
 
 
df0a622
cb5c850
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
df0a622
 
 
cb5c850
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
 
import datetime
import hashlib
import json
import os
import random
from io import BytesIO
import gradio as gr
from datasets import load_dataset
from huggingface_hub import upload_file



dataset = load_dataset("edbeeching/rlhf_dialog_experiment_cosmo_dialog_generation", 
use_auth_token=os.environ['EB_TOKEN'])["validation"]

def sample_to_markdown(sample, index):
    conversation = sample["conversations"][index]
    trucation_length = sample["trucation_length"]
    output = ""
    aligns = ["left", "right"]
    strongs1 = ["", "<strong>"]
    strongs2 = ["", "</strong>"]
    for i,conv in enumerate(conversation):
        if i == trucation_length:
            output += '<p style="text-align:center"> --- START OF DIALOG GENERATION --- </p><br>'
        align = aligns[i%2]
        strong1 = strongs1[i%2]
        strong2 = strongs2[i%2]
        
        output += f'<div style="text-align: {align}"> {strong1}{conv}{strong2} \n </div> <br>'

    return output



sample = None
def get_sample():
    # I set the seed here as the randomness was a bit off otherwise
    print(abs(hash(datetime.datetime.now().strftime("%Y%m%d_%H%M%s"))) % (10 ** 8))
    random.seed(abs(hash(datetime.datetime.now().strftime("%Y%m%d_%H%M%s"))) % (10 ** 8))
    dataset_size = len(dataset)
    sample_index = random.randint(0, dataset_size-1)
    sample = dataset[sample_index]
    return sample

def check_and_submit_preferences(sample, preferred_text, text_quality):
    if preferred_text is None:
        print("not submitted due to unselected preferred text")
        return

    if text_quality is None:
        print("not submitted due to unselected text_quality")
        return

    data = {
        "sample": sample,
        "preferred_text": preferred_text,
        "text_quality": text_quality,
        "date_time": datetime.datetime.now().strftime("%m/%d/%Y %H:%M:%S")
        # add other info like user etc?
    }

    task_hash = hashlib.md5(sample["situation"].encode())
    time_now = datetime.datetime.now().strftime("%Y%m%d_%H%M%s")
    task_directory = f"{time_now}_{task_hash.hexdigest()}"
    upload_file(
                path_or_fileobj=BytesIO(bytes(json.dumps(data), 'utf-8')),
                path_in_repo=task_directory,
                repo_id='edbeeching/rlhf_dialog_experiment_dataset',
                repo_type='dataset',
                token=os.environ['EB_TOKEN']
            )

with gr.Blocks() as demo:
    gr.Markdown(
        """
    This Space is an experiment to model human preferences on dialog generated with the [Cosmo-XL](https://huggingface.co/allenai/cosmo-xl) model, prompted with parts of conversations from the [SODA](https://huggingface.co/datasets/allenai/soda) dataset.
    The following conversation was created with the following prompt:

    """
    )

    sample = get_sample()

    with gr.Column() as details_col:
        summary = gr.Markdown(f"## {sample['situation']}", label='Description')
        with gr.Row():
            with gr.Column():
                with gr.Box():
                    dialog1 = gr.Markdown(sample_to_markdown(sample, 0), label='Dialog 1')

            with gr.Column():
                with gr.Box():
                    dialog2 = gr.Markdown(sample_to_markdown(sample, 1), label='Dialog 2')

        with gr.Column():
            dialog_choice = gr.Radio(["Left dialog", "Right dialog"], label="Preferred text", interactive=True)
            quality_of_dialog = gr.Radio(["Terrible", "Poor", "Ok", "Good", "Excellent"], label="Quality of preferred text", interactive=True)
            next_button = gr.Button("Submit")


    def on_next(preferred_text, text_quality):
        # check and submit the current response
        global sample
        check_and_submit_preferences(sample, preferred_text, text_quality)

        sample = get_sample()

        return (
            gr.Markdown.update(f"## {sample['situation']}"), 
            gr.Markdown.update(sample_to_markdown(sample, 0)), 
            gr.Markdown.update(sample_to_markdown(sample, 1)), 
            gr.Radio.update(value=None),
            gr.Radio.update(value=None)
        )

    next_button.click(on_next, inputs=[dialog_choice, quality_of_dialog], outputs=[summary, dialog1, dialog2, dialog_choice, quality_of_dialog])

if __name__ == "__main__":
    demo.launch()