backup

import pke
from sense2vec import Sense2Vec
import time
import gradio as gr
from transformers import AutoTokenizer
import os
from pathlib import Path
from FastT5 import get_onnx_runtime_sessions, OnnxT5

# commands = [
#     "curl -LO https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz",
#     "tar -xvf  s2v_reddit_2015_md.tar.gz",
# ]

# for command in commands:
#     return_code = os.system(command)
#     if return_code == 0:
#         print(f"Command '{command}' executed successfully")
#     else:
#         print(f"Command '{command}' failed with return code {return_code}")

s2v = Sense2Vec().from_disk("s2v_old")

trained_model_path = './t5_squad_v1/'

pretrained_model_name = Path(trained_model_path).stem

encoder_path = os.path.join(
    trained_model_path, f"{pretrained_model_name}-encoder_quantized.onnx")
decoder_path = os.path.join(
    trained_model_path, f"{pretrained_model_name}-decoder_quantized.onnx")
init_decoder_path = os.path.join(
    trained_model_path, f"{pretrained_model_name}-init-decoder_quantized.onnx")

model_paths = encoder_path, decoder_path, init_decoder_path
model_sessions = get_onnx_runtime_sessions(model_paths)
model = OnnxT5(trained_model_path, model_sessions)

tokenizer = AutoTokenizer.from_pretrained(trained_model_path)


def get_question(sentence, answer, mdl, tknizer):
    text = f"context: {sentence} answer: {answer}"
    print(text)
    max_len = 256
    encoding = tknizer.encode_plus(
        text, max_length=max_len, pad_to_max_length=False, truncation=True, return_tensors="pt")
    input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
    outs = mdl.generate(input_ids=input_ids,
                        attention_mask=attention_mask,
                        early_stopping=True,
                        num_beams=5,
                        num_return_sequences=1,
                        no_repeat_ngram_size=2,
                        max_length=300)

    dec = [tknizer.decode(ids, skip_special_tokens=True) for ids in outs]

    Question = dec[0].replace("question:", "")
    Question = Question.strip()
    return Question


def generate_question(context, answer):
    start_time = time.time()  # Record the start time
    result = get_question(context, answer, model, tokenizer)
    end_time = time.time()    # Record the end time
    latency = end_time - start_time  # Calculate latency
    print(f"Latency: {latency} seconds")
    return result


def generate_mcq(context):
    extractor = pke.unsupervised.TopicRank()
    extractor.load_document(input=context, language='en')
    extractor.candidate_selection(pos={"NOUN", "PROPN", "ADJ"})
    extractor.candidate_weighting()
    keyphrases = extractor.get_n_best(n=10)

    results = []

    for keyword, _ in keyphrases:
        original_keyword = keyword
        keyword = original_keyword.lower().replace(" ", "_")
        sense = s2v.get_best_sense(keyword)

        if sense is not None:
            most_similar = s2v.most_similar(sense, n=2)
            distractors = [word.split("|")[0].lower().replace(
                "_", " ") for word, _ in most_similar]

            question = generate_question(context, original_keyword)

            result = {
                "Question": question,
                "Keyword": original_keyword,
                "Distractor1": distractors[0],
                "Distractor2": distractors[1]
            }

            results.append(result)

    return results


iface = gr.Interface(
    fn=generate_mcq,
    inputs=gr.Textbox(label="Context", type='text'),
    outputs=gr.JSON(value=list),
    title="Questgen AI",
    description="Enter a context to generate MCQs for keywords."
)

iface.launch()