Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| 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 "⚠️Waiting for documents..." | |
| 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 | |
| - Texto original: | |
| This tool will enable asking questions of your uploaded text or PDF documents. | |
| It uses OpenAI's GPT models and thus you must enter your API key below. This | |
| tool is under active development and currently uses many tokens - up to 10,000 | |
| for a single query. That is $0.10-0.20 per query, so please be careful! | |
| * [PaperQA](https://github.com/whitead/paper-qa) is the code used to build this tool. | |
| * [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes. | |
| ## 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="Documents and Citations" | |
| ) | |
| buildb = gr.Textbox("⚠️Esperando documentos...", | |
| label="Status", 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("Pregunte") | |
| 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]) | |
| demo.queue(concurrency_count=20) | |
| demo.launch(show_error=True) | |