import gradio as gr
from transformers import pipeline
import numpy as np

model = pipeline("question-answering", model="Eitanli/distilbert-qa-checkpoint-v5")


questions = ['which ingredients are mentioned?',
             'what is the amount of ingredient mentioned?',
             'what are the special instructions mentioned?']


def predict(context, topk, answer_threshold):
    output = 'Recipe analysis:'
    for question in questions:
        pred = model(question=question, context=context, topk=topk)
        answers = '\n'.join([f"{ans['answer']} ({np.round(ans['score'], 2)})" for ans in pred if ans['score'] > answer_threshold])
        output += f'\n\n{question}:\n{answers}'
    return output


iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label="Recipe line"),
        gr.Slider(1, 5, step=1.0, value=2, label="top k", info="Choose between 1 and 5"),
        gr.Slider(0, 0.99, step=0.01, value=0.8, label="answer_threshold", info="Select a threshold in [0, 0.99]")],
    outputs=gr.Textbox(label='Questions and answers')
)
iface.launch()