from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    squad_convert_examples_to_features
)

from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
from transformers.data.metrics.squad_metrics import compute_predictions_logits
import streamlit as st
import gradio as gr
import json
import torch
import time
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

model_checkpoint = "akdeniz27/roberta-base-cuad"

@st.cache(allow_output_mutation=True)
def run_prediction(question_texts, context_text, model_path):
    max_seq_length = 512
    doc_stride = 256
    n_best_size = 1
    max_query_length = 64
    max_answer_length = 512
    do_lower_case = False
    null_score_diff_threshold = 0.0
    def to_list(tensor):
        return tensor.detach().cpu().tolist()
    config_class, model_class, tokenizer_class = (
        AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
    config = config_class.from_pretrained(model_path)
    tokenizer = tokenizer_class.from_pretrained(
        model_path, do_lower_case=True, use_fast=False)
    model = model_class.from_pretrained(model_path, config=config)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    processor = SquadV2Processor()
    examples = []
    for i, question_text in enumerate(question_texts):
        example = SquadExample(
            qas_id=str(i),
            question_text=question_text,
            context_text=context_text,
            answer_text=None,
            start_position_character=None,
            title="Predict",
            answers=None,
        )
        examples.append(example)
    features, dataset = squad_convert_examples_to_features(
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=doc_stride,
        max_query_length=max_query_length,
        is_training=False,
        return_dataset="pt",
        threads=1,
    )
    eval_sampler = SequentialSampler(dataset)
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
    all_results = []
    for batch in eval_dataloader:
        model.eval()
        batch = tuple(t.to(device) for t in batch)
        with torch.no_grad():
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
                "token_type_ids": batch[2],
            }
            example_indices = batch[3]
            outputs = model(**inputs)
            for i, example_index in enumerate(example_indices):
                eval_feature = features[example_index.item()]
                unique_id = int(eval_feature.unique_id)
                output = [to_list(output[i]) for output in outputs.to_tuple()]
                start_logits, end_logits = output
                result = SquadResult(unique_id, start_logits, end_logits)
                all_results.append(result)
    final_predictions = compute_predictions_logits(
        all_examples=examples,
        all_features=features,
        all_results=all_results,
        n_best_size=n_best_size,
        max_answer_length=max_answer_length,
        do_lower_case=do_lower_case,
        output_prediction_file=None,
        output_nbest_file=None,
        output_null_log_odds_file=None,
        verbose_logging=False,
        version_2_with_negative=True,
        null_score_diff_threshold=null_score_diff_threshold,
        tokenizer=tokenizer
    )
    return final_predictions

@st.cache(allow_output_mutation=True)
def load_model():
    model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint , use_fast=False)
    return model, tokenizer
    
@st.cache(allow_output_mutation=True)
def load_questions():
	with open('test.json') as json_file:
		data = json.load(json_file)
	questions = []
	for i, q in enumerate(data['data'][0]['paragraphs'][0]['qas']):
		question = data['data'][0]['paragraphs'][0]['qas'][i]['question']
		questions.append(question)
	return questions
	
@st.cache(allow_output_mutation=True)
def load_contracts():
	with open('test.json') as json_file:
		data = json.load(json_file)
	contracts = []
	for i, q in enumerate(data['data']):
		contract = ' '.join(data['data'][i]['paragraphs'][0]['context'].split())
		contracts.append(contract)
	return contracts
	
model, tokenizer = load_model()
questions = load_questions()
contracts = load_contracts()
contract = contracts[0]

st.header("📚 Question Answering in Contract Understanding Atticus Dataset (CUAD)")
st.image("contract_review.png")

selected_question = st.selectbox('Choose one of the queries from the CUAD dataset or write a legal contract and see if the model can answer correctly. The model only supports English Language:', questions)

question_set = [questions[0], selected_question]
contract_type = st.radio("Select Contract", ("Sample Contract", "New Contract"))

if contract_type == "📖 Sample Contract":
	sample_contract_num = st.slider("Select Sample Contract #")
	contract = contracts[sample_contract_num]
	with st.expander(f"Sample Contract #{sample_contract_num}"):
		st.write(contract)
else:
	contract = st.text_area("📝 Input New Contract", "", height=256)
Run_Button = st.button("Run", key=None)
if Run_Button == True and not len(contract)==0 and not len(question_set)==0:
	predictions = run_prediction(question_set, contract, 'akdeniz27/roberta-base-cuad')
	
	for i, p in enumerate(predictions):
		if i != 0: st.write(f"Question: {question_set[int(p)]}\n\nAnswer: {predictions[p]}\n\n")


st.write("Credits: ")
st.write("Model: akdeniz27/roberta-base-cuad")
st.write("Project: https://www.atticusprojectai.org/cuad")
st.write("Git Hub: https://github.com/TheAtticusProject/cuad")
st.write("CUAD Dataset: https://huggingface.co/datasets/cuad")
st.write("Based on https://github.com/marshmellow77/cuad-demo")