File size: 8,187 Bytes
0f36bab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bca6282
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0f36bab
bca6282
 
 
 
 
 
 
 
0f36bab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e0ac9f6
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
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
import json
import os
import random
import string

import gradio as gr
import huggingface_hub
from datasets import load_dataset
from evaluate import load

from guidelines import guidelines

human2_annotation_file = "human2/test-human2.jsonl"
space = "Iker/ClickbaitAnnotation"


def clean_text(text: str) -> str:
    # Remove punctuation
    text = text.translate(str.maketrans("", "", string.punctuation))

    # Remove newlines and multiple spaces
    text = text.replace("\n", " ").strip()
    text = " ".join(text.split()).strip()

    # lowercase
    text = text.lower()

    return text


def html_progress_bar(completed_steps, total_steps):
    percentage = (completed_steps / total_steps) * 100
    return f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Progress Bar</title>
    <style>
        .progress-container {{
            width: 100%;
            background-color: #ffffff;
        }}

        .progress-bar {{
            width: {percentage}%;
            height: 30px;
            background-color: #d1fae5;
            text-align: center;
            line-height: 30px;
            color: white;
        }}
    </style>
    </head>
    <body>
    
    <div class="progress-container">
        <div class="progress-bar">{percentage:.0f}%</div>
    </div>

    </body>
    </html>
    """


class AnnotationManager:
    def __init__(self):
        self.dataset = list(
            load_dataset(
                "Iker/NoticIA", token=os.environ.get("TOKEN") or True, split="test"
            )
        )

        self.total = len(self.dataset)
        self.predictions = []
        self.references = []

        print(f"Total examples: {self.total}")

        try:
            huggingface_hub.hf_hub_download(
                repo_id=space,
                repo_type="space",
                token=os.environ.get("TOKEN") or True,
                filename=human2_annotation_file,
                local_dir=os.getcwd(),
            )

            with open(human2_annotation_file, "r") as f:
                annotations = f.readlines()

            annotations = [json.loads(a) for a in annotations]
            for a in annotations:
                self.predictions.append(clean_text(a["summary2"]))
                self.references.append([clean_text(a["summary"])])

            self.dataset = self.dataset[len(annotations) :]
        except Exception:
            print("Unable to download annotations. Starting from the beginning.")

        self.current = None

    def get_next(self):
        if len(self.dataset) == 0:
            return "🎉 Anotación Finalizada 🎉", "🎉 Anotación Finalizada 🎉"
        self.current = self.dataset.pop(0)
        return self.current["web_headline"], self.current["web_text"]

    def save_annotation(self, annotation):
        if len(annotation) > 0:
            example = {
                "web_url": self.current["web_url"],
                "web_headline": self.current["web_headline"],
                "summary": self.current["summary"],
                "summary2": annotation,
                "web_text": self.current["web_text"],
                "clean_web_text": self.current["clean_web_text"],
            }

            if not os.path.exists(human2_annotation_file):
                os.makedirs(os.path.dirname(human2_annotation_file), exist_ok=True)
                with open(human2_annotation_file, "w", encoding="utf8") as f:
                    print(json.dumps(example, ensure_ascii=False), file=f)
            else:
                with open(human2_annotation_file, "a", encoding="utf8") as f:
                    print(json.dumps(example, ensure_ascii=False), file=f)

            self.predictions.append(clean_text(annotation))
            self.references.append([clean_text(example["summary"])])

            huggingface_hub.upload_file(
                repo_id=space,
                repo_type="space",
                token=os.environ.get("TOKEN") or True,
                path_in_repo=human2_annotation_file,
                path_or_fileobj=human2_annotation_file,
            )

            next_headline, next_text = self.get_next()
            return (
                next_headline,
                next_text,
                self.get_rouge(),
                self.progress(),
                "",
            )

        if self.current is not None:
            return (
                self.current["web_headline"],
                self.current["web_text"],
                self.get_rouge(),
                self.progress(),
                "",
            )

        else:
            return (
                "Pulsa ▶️",
                "Pulsa ▶️",
                "Pulsa ▶️",
                self.progress(),
                "",
            )

    def get_rouge(self):
        try:
            experiment_id = "".join(
                random.choice(string.ascii_uppercase + string.digits) for _ in range(6)
            )
            rouge = load("rouge", experiment_id=experiment_id)

            return rouge.compute(
                predictions=self.predictions,
                references=self.references,
                use_aggregator=True,
                rouge_types=["rouge1"],
            )["rouge1"]
        except Exception:
            return "N/A"

    def progress(self):
        # Return  first number represents steps completed, and second value represents total steps
        return html_progress_bar(self.total - len(self.dataset), self.total)

    def gr_start(self):
        if self.current is not None:
            return (
                self.current["web_headline"],
                self.current["web_text"],
                self.get_rouge(),
                self.progress(),
                "",
            )
        headline, text = self.get_next()
        return headline, text, self.get_rouge(), self.progress(), ""


theme = gr.themes.Soft(
    primary_hue="emerald",
    secondary_hue="red",
    text_size="sm",
    spacing_size="sm",
    font=[
        gr.themes.GoogleFont("Poppins"),
        gr.themes.GoogleFont("Poppins"),
        gr.themes.GoogleFont("Poppins"),
        gr.themes.GoogleFont("Poppins"),
    ],
).set(block_background_fill="*neutral_50", block_background_fill_dark="*neutral_950")

manager = AnnotationManager()


with gr.Blocks(
    theme=theme, title="🖱️ Resumen de noticias Clickbait 🖱️", analytics_enabled=False
) as demo:
    with gr.Tab("Guidelines") as tab_guidelines:
        gr.Markdown(guidelines)

    with gr.Tab("Anotación") as tab_annotation:
        gr_play = gr.Button("▶️ Empieza a anotar")

        gr_progress = gr.HTML(value=manager.progress(), label="Progreso")

        gr_rouge = gr.Textbox(
            value="Pulsa ▶️",
            label="Rouge-1",
            info="Rouge Score actual entre las anotaciones y los resúmenes de referencia.",
            lines=1,
            interactive=False,
        )

        gr_headline = gr.Textbox(
            value="Pulsa ▶️",
            label="Titular",
            info="El titular del artículo.",
            lines=2,
            interactive=False,
        )

        gr_body = gr.Textbox(
            value="Pulsa ▶️",
            label="Artículo",
            info="El cuerpo del artículo/noticia.",
            lines=10,
            interactive=False,
        )

        gr_summary = gr.Textbox(
            value="",
            label="Resumen",
            info="Escribe aquí el resumen del artículo. Recuerda leer las guidelines antes de empezar.",
            lines=2,
            interactive=True,
        )

        save = gr.Button(
            "💾 Guardar",
        )

        save.click(
            fn=manager.save_annotation,
            inputs=[gr_summary],
            outputs=[gr_headline, gr_body, gr_rouge, gr_progress, gr_summary],
        )

        gr_play.click(
            fn=manager.gr_start,
            inputs=None,
            outputs=[gr_headline, gr_body, gr_rouge, gr_progress, gr_summary],
        )


demo.launch(auth=(os.environ.get("pass"), os.environ.get("pass")))