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| import gradio as gr | |
| from pathlib import Path | |
| import os | |
| os.system('pip install transformers') | |
| os.system('pip install --upgrade pip') | |
| os.system('pip install tensorflow') | |
| from transformers import pipeline | |
| docs = None | |
| def request_pathname(files): | |
| if files is None: | |
| return [[]] | |
| return [[file.name, file.name.split('/')[-1]] for file in files] | |
| def validate_dataset(dataset): | |
| global docs | |
| docs = None # clear it out if dataset is modified | |
| docs_ready = dataset.iloc[-1, 0] != "" | |
| if docs_ready: | |
| return "✨Listo✨" | |
| else: | |
| return "⚠️Esperando documentos..." | |
| def do_ask(question, button, dataset): | |
| global docs | |
| docs_ready = dataset.iloc[-1, 0] != "" | |
| if button == "✨Listo✨" and docs_ready: | |
| for _, row in dataset.iterrows(): | |
| path = row['filepath'] | |
| text = Path(f'{path}').read_text() | |
| question_answerer = pipeline("question-answering", model='distilbert-base-cased-distilled-squad') | |
| QA_input = { | |
| 'question': question, | |
| 'context': text | |
| } | |
| return question_answerer(QA_input)['answer'] | |
| else: | |
| return "" | |
| # def do_ask(question, button, dataset, progress=gr.Progress()): | |
| # global docs | |
| # docs_ready = dataset.iloc[-1, 0] != "" | |
| # if button == "✨Listo✨" and docs_ready: | |
| # if docs is None: # don't want to rebuild index if it's already built | |
| # import paperqa | |
| # docs = paperqa.Docs() | |
| # # dataset is pandas dataframe | |
| # for _, row in dataset.iterrows(): | |
| # key = None | |
| # if ',' not in row['citation string']: | |
| # key = row['citation string'] | |
| # docs.add(row['filepath'], row['citation string'], key=key) | |
| # else: | |
| # return "" | |
| # progress(0, "Construyendo índices...") | |
| # docs._build_faiss_index() | |
| # progress(0.25, "Encolando...") | |
| # result = docs.query(question) | |
| # progress(1.0, "¡Hecho!") | |
| # return result.formatted_answer, result.context | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Document Question and Answer adaptado al castellano por Pablo Ascorbe. | |
| Este espacio ha sido clonado y adaptado de: https://huggingface.co/spaces/whitead/paper-qa | |
| La idea es utilizar un modelo preentrenado de HuggingFace como "distilbert-base-cased-distilled-squad" | |
| y responder las preguntas en inglés, para ello, será necesario hacer primero una traducción de los textos en castellano | |
| a inglés y luego volver a traducir en sentido contrario. | |
| ## Instrucciones: | |
| Adjunte su documento, ya sea en formato .txt o .pdf, y pregunte lo que desee. | |
| """) | |
| uploaded_files = gr.File( | |
| label="Sus documentos subidos (PDF o txt)", file_count="multiple", ) | |
| dataset = gr.Dataframe( | |
| headers=["filepath", "citation string"], | |
| datatype=["str", "str"], | |
| col_count=(2, "fixed"), | |
| interactive=True, | |
| label="Documentos y citas" | |
| ) | |
| buildb = gr.Textbox("⚠️Esperando documentos...", | |
| label="Estado", interactive=False, show_label=True) | |
| dataset.change(validate_dataset, inputs=[ | |
| dataset], outputs=[buildb]) | |
| uploaded_files.change(request_pathname, inputs=[ | |
| uploaded_files], outputs=[dataset]) | |
| query = gr.Textbox( | |
| placeholder="Introduzca su pregunta aquí...", label="Pregunta") | |
| ask = gr.Button("Preguntar") | |
| gr.Markdown("## Respuesta") | |
| answer = gr.Markdown(label="Respuesta") | |
| with gr.Accordion("Contexto", open=False): | |
| gr.Markdown( | |
| "### Contexto\n\nEl siguiente contexto ha sido utilizado para generar la respuesta:") | |
| context = gr.Markdown(label="Contexto") | |
| # ask.click(fn=do_ask, inputs=[query, buildb, | |
| # dataset], outputs=[answer, context]) | |
| ask.click(fn=do_ask, inputs=[query, buildb, | |
| dataset], outputs=[answer]) | |
| demo.queue(concurrency_count=20) | |
| demo.launch(show_error=True) | |