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| import os | |
| import gradio as gr | |
| from gradio.components import Textbox, Button, Slider, Checkbox | |
| from AinaTheme import theme | |
| from urllib.error import HTTPError | |
| from rag import RAG | |
| from utils import setup | |
| MAX_NEW_TOKENS = 700 | |
| SHOW_MODEL_PARAMETERS_IN_UI = os.environ.get("SHOW_MODEL_PARAMETERS_IN_UI", default="True") == "True" | |
| setup() | |
| rag = RAG( | |
| hf_token=os.getenv("HF_TOKEN"), | |
| embeddings_model=os.getenv("EMBEDDINGS"), | |
| model_name=os.getenv("MODEL"), | |
| ) | |
| def generate(prompt, model_parameters): | |
| try: | |
| output, context, source = rag.get_response(prompt, model_parameters) | |
| return output, context, source | |
| except HTTPError as err: | |
| if err.code == 400: | |
| gr.Warning( | |
| "The inference endpoint is only available Monday through Friday, from 08:00 to 20:00 CET." | |
| ) | |
| except: | |
| gr.Warning( | |
| "Inference endpoint is not available right now. Please try again later." | |
| ) | |
| def submit_input(input_, num_chunks, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature): | |
| if input_.strip() == "": | |
| gr.Warning("Not possible to inference an empty input") | |
| return None | |
| model_parameters = { | |
| "NUM_CHUNKS": num_chunks, | |
| "max_new_tokens": max_new_tokens, | |
| "repetition_penalty": repetition_penalty, | |
| "top_k": top_k, | |
| "top_p": top_p, | |
| "do_sample": do_sample, | |
| "temperature": temperature | |
| } | |
| output, context, source = generate(input_, model_parameters) | |
| sources_markup = "" | |
| for url in source: | |
| sources_markup += f'<a href="{url}" target="_blank">{url}</a><br>' | |
| return output.strip(), sources_markup, context | |
| def change_interactive(text): | |
| if len(text) == 0: | |
| return gr.update(interactive=True), gr.update(interactive=False) | |
| return gr.update(interactive=True), gr.update(interactive=True) | |
| def clear(): | |
| return ( | |
| None, | |
| None, | |
| None, | |
| None, | |
| None, | |
| gr.Slider(value=MAX_NEW_TOKENS), | |
| gr.Slider(value=1.0), | |
| gr.Slider(value=50), | |
| gr.Slider(value=0.99), | |
| gr.Checkbox(value=False), | |
| gr.Slider(value=0.35), | |
| ) | |
| def gradio_app(): | |
| with gr.Blocks(theme=theme) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=0.1): | |
| gr.Image("rag_image.jpg", elem_id="flor-banner", scale=1, height=256, width=256, show_label=False, show_download_button = False, show_share_button = False) | |
| with gr.Column(): | |
| gr.Markdown( | |
| """# Demo de Retrieval-Augmented Generation per documents legals | |
| 🔍 **Retrieval-Augmented Generation** (RAG) és una tecnologia de IA que permet interrogar un repositori de documents amb preguntes | |
| en llenguatge natural, i combina tècniques de recuperació d'informació avançades amb models generatius per redactar una resposta | |
| fent servir només la informació existent en els documents del repositori. | |
| 🎯 **Objectiu:** Aquest és un primer demostrador amb la normativa vigent publicada al Diari Oficial de la Generalitat de Catalunya, en el | |
| repositori del EADOP (Entitat Autònoma del Diari Oficial i de Publicacions). Aquesta primera versió explora prop de 2000 documents en català, | |
| i genera la resposta fent servir el model Flor6.3b entrenat amb el dataset de QA generativa projecte-aina/RAG_Multilingual. | |
| ⚠️ **Advertencies**: Primera versió experimental. El contingut generat per aquest model no està supervisat i pot ser incorrecte. | |
| Si us plau, tingueu-ho en compte quan exploreu aquest recurs. | |
| """ | |
| ) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(variant="panel"): | |
| input_ = Textbox( | |
| lines=11, | |
| label="Input", | |
| placeholder="Quina és la finalitat del Servei Meteorològic de Catalunya?", | |
| # value = "Quina és la finalitat del Servei Meteorològic de Catalunya?" | |
| ) | |
| with gr.Row(variant="panel"): | |
| clear_btn = Button( | |
| "Clear", | |
| ) | |
| submit_btn = Button("Submit", variant="primary", interactive=False) | |
| with gr.Row(variant="panel"): | |
| with gr.Accordion("Model parameters", open=False, visible=SHOW_MODEL_PARAMETERS_IN_UI): | |
| num_chunks = Slider( | |
| minimum=1, | |
| maximum=6, | |
| step=1, | |
| value=2, | |
| label="Number of chunks" | |
| ) | |
| max_new_tokens = Slider( | |
| minimum=50, | |
| maximum=2000, | |
| step=1, | |
| value=MAX_NEW_TOKENS, | |
| label="Max tokens" | |
| ) | |
| repetition_penalty = Slider( | |
| minimum=0.1, | |
| maximum=2.0, | |
| step=0.1, | |
| value=1.0, | |
| label="Repetition penalty" | |
| ) | |
| top_k = Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Top k" | |
| ) | |
| top_p = Slider( | |
| minimum=0.01, | |
| maximum=0.99, | |
| value=0.99, | |
| label="Top p" | |
| ) | |
| do_sample = Checkbox( | |
| value=False, | |
| label="Do sample" | |
| ) | |
| temperature = Slider( | |
| minimum=0.1, | |
| maximum=1, | |
| value=0.35, | |
| label="Temperature" | |
| ) | |
| parameters_compontents = [num_chunks, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature] | |
| with gr.Column(variant="panel"): | |
| output = Textbox( | |
| lines=10, | |
| label="Output", | |
| interactive=False, | |
| show_copy_button=True | |
| ) | |
| with gr.Accordion("Sources and context:", open=False): | |
| source_context = gr.Markdown( | |
| label="Sources", | |
| show_label=False, | |
| ) | |
| with gr.Accordion("See full context evaluation:", open=False): | |
| context_evaluation = gr.Markdown( | |
| label="Full context", | |
| show_label=False, | |
| # interactive=False, | |
| # autoscroll=False, | |
| # show_copy_button=True | |
| ) | |
| input_.change( | |
| fn=change_interactive, | |
| inputs=[input_], | |
| outputs=[clear_btn, submit_btn], | |
| api_name=False, | |
| ) | |
| input_.change( | |
| fn=None, | |
| inputs=[input_], | |
| api_name=False, | |
| js="""(i, m) => { | |
| document.getElementById('inputlenght').textContent = i.length + ' ' | |
| document.getElementById('inputlenght').style.color = (i.length > m) ? "#ef4444" : ""; | |
| }""", | |
| ) | |
| clear_btn.click( | |
| fn=clear, | |
| inputs=[], | |
| outputs=[input_, output, source_context, context_evaluation] + parameters_compontents, | |
| queue=False, | |
| api_name=False | |
| ) | |
| submit_btn.click( | |
| fn=submit_input, | |
| inputs=[input_]+ parameters_compontents, | |
| outputs=[output, source_context, context_evaluation], | |
| api_name="get-results" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| gr.Examples( | |
| examples=[ | |
| ["""Què és l'EADOP (Entitat Autònoma del Diari Oficial i de Publicacions)?"""], | |
| ], | |
| inputs=input_, | |
| outputs=[output, source_context, context_evaluation], | |
| fn=submit_input, | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["""Què diu el decret sobre la senyalització de les begudes alcohòliques i el tabac a Catalunya?"""], | |
| ], | |
| inputs=input_, | |
| outputs=[output, source_context, context_evaluation], | |
| fn=submit_input, | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["""Com es pot inscriure una persona al Registre de catalans i catalanes residents a l'exterior?"""], | |
| ], | |
| inputs=input_, | |
| outputs=[output, source_context, context_evaluation], | |
| fn=submit_input, | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["""Quina és la finalitat del Servei Meterològic de Catalunya ?"""], | |
| ], | |
| inputs=input_, | |
| outputs=[output, source_context, context_evaluation], | |
| fn=submit_input, | |
| ) | |
| demo.launch(show_api=True) | |
| if __name__ == "__main__": | |
| gradio_app() |