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| import os | |
| import requests | |
| import gradio as gr | |
| from download_url import download_text_and_title | |
| from cache_system import CacheHandler | |
| from collections import OrderedDict | |
| from typing import Any, Iterable, List | |
| import datetime | |
| import json | |
| server = os.environ.get("SERVER") or "http://localhost:7861/generate" | |
| auth_token = os.environ.get("TOKEN") or True | |
| API_KEY = os.environ.get("API_KEY") or None | |
| total_runs = 0 | |
| def call_vllm_server(tittle, body, mode, stream=True): | |
| api_url = server | |
| headers = {"User-Agent": "Test Client"} | |
| json = { | |
| "n": 1, | |
| "tittle": tittle, | |
| "body": body, | |
| "mode": mode, | |
| "max_tokens": 4096, | |
| "temperature": 0.15, | |
| "top_p": 0.1, | |
| "top_k": 40, | |
| "repetition_penalty": 1.1, | |
| "stop": [ | |
| "<s>", | |
| "</s>", | |
| "\\n", | |
| "<|im_end|>", | |
| ], | |
| "stream": stream, | |
| "api_key": API_KEY, | |
| } | |
| response = requests.post(api_url, headers=headers, json=json) | |
| return response | |
| def get_streaming_response(response: requests.Response) -> Iterable[List[str]]: | |
| for chunk in response.iter_lines( | |
| chunk_size=8192, decode_unicode=False, delimiter=b"\0" | |
| ): | |
| if chunk: | |
| data = json.loads(chunk.decode("utf-8")) | |
| output = data["text"] | |
| yield output | |
| class HuggingFaceDatasetSaver_custom(gr.HuggingFaceDatasetSaver): | |
| def _deserialize_components( | |
| self, | |
| data_dir, | |
| flag_data: list[Any], | |
| flag_option: str = "", | |
| username: str = "", | |
| ) -> tuple[dict[Any, Any], list[Any]]: | |
| """Deserialize components and return the corresponding row for the flagged sample. | |
| Images/audio are saved to disk as individual files. | |
| """ | |
| # Components that can have a preview on dataset repos | |
| file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"} | |
| # Generate the row corresponding to the flagged sample | |
| features = OrderedDict() | |
| row = [] | |
| for component, sample in zip(self.components, flag_data): | |
| label = component.label or "" | |
| features[label] = {"dtype": "string", "_type": "Value"} | |
| row.append(sample) | |
| features["flag"] = {"dtype": "string", "_type": "Value"} | |
| features["username"] = {"dtype": "string", "_type": "Value"} | |
| row.append(flag_option) | |
| row.append(username) | |
| return features, row | |
| def finish_generation(text: str) -> str: | |
| return f"{text}\n\n⬇️ Ayuda a mejorar la herramienta marcando si el resumen es correcto o no.⬇️" | |
| def generate_text( | |
| url: str, mode: int, progress=gr.Progress(track_tqdm=False) | |
| ) -> (str, str): | |
| global cache_handler | |
| global total_runs | |
| total_runs += 1 | |
| print(f"Total runs: {total_runs}. Last run: {datetime.datetime.now()}") | |
| url = url.strip() | |
| if url.startswith("https://twitter.com/") or url.startswith("https://x.com/"): | |
| yield ( | |
| "🤖 Vaya, parece que has introducido la url de un tweet. No puedo acceder a tweets, tienes que introducir la URL de una noticia.", | |
| "❌❌❌ Si el tweet contiene una noticia, dame la URL de la noticia ❌❌❌", | |
| "Error", | |
| ) | |
| return ( | |
| "🤖 Vaya, parece que has introducido la url de un tweet. No puedo acceder a tweets, tienes que introducir la URL de una noticia.", | |
| "❌❌❌ Si el tweet contiene una noticia, dame la URL de la noticia ❌❌❌", | |
| "Error", | |
| ) | |
| # 1) Download the article | |
| progress(0, desc="🤖 Accediendo a la noticia") | |
| # First, check if the URL is in the cache | |
| title, text, temp = cache_handler.get_from_cache(url, mode) | |
| if title is not None and text is not None and temp is not None: | |
| temp = finish_generation(temp) | |
| yield title, temp, text | |
| return title, temp, text | |
| else: | |
| try: | |
| title, text, url = download_text_and_title(url) | |
| except Exception as e: | |
| print(e) | |
| title = None | |
| text = None | |
| if title is None or text is None: | |
| yield ( | |
| "🤖 No he podido acceder a la notica, asegurate que la URL es correcta y que es posible acceder a la noticia desde un navegador.", | |
| "❌❌❌ Inténtalo de nuevo ❌❌❌", | |
| "Error", | |
| ) | |
| return ( | |
| "🤖 No he podido acceder a la notica, asegurate que la URL es correcta y que es posible acceder a la noticia desde un navegador.", | |
| "❌❌❌ Inténtalo de nuevo ❌❌❌", | |
| "Error", | |
| ) | |
| # Test if the redirected and clean url is in the cache | |
| _, _, temp = cache_handler.get_from_cache(url, mode, second_try=True) | |
| if temp is not None: | |
| temp = finish_generation(temp) | |
| yield title, temp, text | |
| return title, temp, text | |
| progress(0.5, desc="🤖 Leyendo noticia") | |
| try: | |
| response = call_vllm_server(title, text, mode, stream=True) | |
| for h in get_streaming_response(response): | |
| temp = h[0] | |
| yield title, temp, text | |
| except Exception as e: | |
| print(e) | |
| yield ( | |
| "🤖 El servidor no se encuentra disponible.", | |
| "❌❌❌ Inténtalo de nuevo más tarde ❌❌❌", | |
| "Error", | |
| ) | |
| return ( | |
| "🤖 El servidor no se encuentra disponible.", | |
| "❌❌❌ Inténtalo de nuevo más tarde ❌❌❌", | |
| "Error", | |
| ) | |
| cache_handler.add_to_cache( | |
| url=url, title=title, text=text, summary_type=mode, summary=temp | |
| ) | |
| temp = finish_generation(temp) | |
| yield title, temp, text | |
| hits, misses, cache_len = cache_handler.get_cache_stats() | |
| print( | |
| f"Hits: {hits}, misses: {misses}, cache length: {cache_len}. Percent hits: {round(hits/(hits+misses)*100,2)}%." | |
| ) | |
| return title, temp, text | |
| cache_handler = CacheHandler(max_cache_size=1000) | |
| hf_writer = HuggingFaceDatasetSaver_custom( | |
| auth_token, "Iker/Clickbait-News", private=True, separate_dirs=False | |
| ) | |
| demo = gr.Interface( | |
| generate_text, | |
| inputs=[ | |
| gr.Textbox( | |
| label="🌐 URL de la noticia", | |
| info="Introduce la URL de la noticia que deseas resumir.", | |
| value="https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/", | |
| interactive=True, | |
| ), | |
| gr.Slider( | |
| minimum=0, | |
| maximum=100, | |
| step=50, | |
| value=50, | |
| label="🎚️ Nivel de resumen", | |
| info="""¿Hasta qué punto quieres resumir la noticia? | |
| Si solo deseas un resumen, selecciona 0. | |
| Si buscas un resumen y desmontar el clickbait, elige 50. | |
| Para obtener solo la respuesta al clickbait, selecciona 100""", | |
| interactive=True, | |
| ), | |
| ], | |
| outputs=[ | |
| gr.Textbox( | |
| label="📰 Titular de la noticia", | |
| interactive=False, | |
| placeholder="Aquí aparecerá el título de la noticia", | |
| ), | |
| gr.Textbox( | |
| label="🗒️ Resumen", | |
| interactive=False, | |
| placeholder="Aquí aparecerá el resumen de la noticia.", | |
| ), | |
| gr.Textbox( | |
| label="Noticia completa", | |
| visible=False, | |
| render=False, | |
| interactive=False, | |
| placeholder="Aquí aparecerá el resumen de la noticia.", | |
| ), | |
| ], | |
| # title="⚔️ Clickbait Fighter! ⚔️", | |
| thumbnail="https://huggingface.co/spaces/Iker/ClickbaitFighter/resolve/main/logo2.png", | |
| theme="JohnSmith9982/small_and_pretty", | |
| description=""" | |
| <table> | |
| <tr> | |
| <td style="width:100%"><img src="https://huggingface.co/spaces/Iker/ClickbaitFighter/resolve/main/head.png" align="right" width="100%"> </td> | |
| </tr> | |
| </table> | |
| <p align="center"> <a href="https://www.omegaai.io/"> <img src="https://huggingface.co/spaces/Iker/ClickbaitFighter/resolve/main/omegaai.png" align="center" width="15%"> </a> <a href="https://0dai.omegaai.io/"> <img src="https://huggingface.co/spaces/Iker/ClickbaitFighter/resolve/main/0dai.png" align="center" width="15%"> </a></p> | |
| <p align="justify">Esta Inteligencia Artificial es capaz de generar un resumen de una sola frase que revela la verdad detrás de un titular sensacionalista o clickbait. Solo tienes que introducir la URL de la noticia. La IA accederá a la noticia, la leerá y en cuestión de segundos generará un resumen de una sola frase que revele la verdad detrás del titular.</p> | |
| 🎚 Ajusta el nivel de resumen con el control deslizante. Cuanto maś alto, más corto será el resumen. | |
| ⌚ La IA se encuentra corriendo en un hardware bastante modesto, debería tardar menos de 30 segundos en generar el resumen, pero si muchos usuarios usan la app a la vez, tendrás que esperar tu turno. | |
| 💸 Este es un projecto sin ánimo de lucro, no se genera ningún tipo de ingreso con esta app. Los datos, la IA y el código se publicarán para su uso en la investigación académica. No puedes usar esta app para ningún uso comercial. | |
| 🧪 El modelo se encuentra en fase de desarrollo, si quieres ayudar a mejorarlo puedes usar los botones 👍 y 👎 para valorar el resumen. ¡Gracias por tu ayuda!""", | |
| article="Esta Inteligencia Artificial ha sido generada por Iker García-Ferrero. Puedes saber más sobre mi trabajo en mi [página web](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) o mi perfil de [X](https://twitter.com/iker_garciaf). Puedes ponerte en contacto conmigo a través de correo electrónico (ver web) y X.", | |
| cache_examples=False, | |
| allow_flagging="manual", | |
| flagging_options=[("👍", "correct"), ("👎", "incorrect")], | |
| flagging_callback=hf_writer, | |
| concurrency_limit=20, | |
| ) | |
| demo.queue(max_size=None) | |
| demo.launch(share=False) | |