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

    - 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="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)