import numpy as np
import gradio as gr
from imports import *
from huggingface_hub import login
login(token="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")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dict_ = {
    0: "negative",
    1: "positive",
    2: "neutral"}
tokenizer_sent = AutoTokenizer.from_pretrained("nam194/sentiment", use_fast=False)
model_sent = AutoModelForSequenceClassification.from_pretrained("nam194/sentiment", num_labels=3, use_auth_token=True).to(device)
def cvt2cls(data):
    data = list(set(data))
    try: 
        data.remove(20)
    except:
        pass
    for i, num in enumerate(data):
        if num == 20:
            continue
        if num>=10:
            data[i] -= 10
    return data
ner_tags = {0: 'B-chỗ để xe', 1: 'B-con người', 2: 'B-công việc', 3: 'B-cơ sở vật chất', 4: 'B-dự án', 5: 'B-lương', 6: 'B-môi trường làm việc', 7: 'B-ot/thời gian', 8: 'B-văn phòng', 9: 'B-đãi ngộ', 10: 'I-chỗ để xe', 11: 'I-con người', 12: 'I-công việc', 13: 'I-cơ sở vật chất', 14: 'I-dự án', 15: 'I-lương', 16: 'I-môi trường làm việc', 17: 'I-ot/thời gian', 18: 'I-văn phòng', 19: 'I-đãi ngộ', 20: 'O'}
topic_tags = {0: 'chỗ để xe', 1: 'con người', 2: 'công việc', 3: 'cơ sở vật chất', 4: 'dự án', 5: 'lương', 6: 'môi trường làm việc', 7: 'ot/thời gian', 8: 'văn phòng', 9: 'đãi ngộ'}
config = RobertaConfig.from_pretrained("nam194/ner", num_labels=21)
tokenizer_topic = AutoTokenizer.from_pretrained("nam194/ner", use_fast=False) 
model_topic = PhoBertLstmCrf.from_pretrained("nam194/ner", config=config, from_tf=False).to(device)
model_topic.resize_token_embeddings(len(tokenizer_topic)) 


def sentiment(sent: str):
    sent_ = normalize(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
    input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
    with torch.no_grad():
        out_sent = model_sent(input_sent)
        logits_sent = out_sent.logits.softmax(dim=-1).tolist()[0]
        pred_sent = dict_[np.argmax(logits_sent)]

    sent = replace_all(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
    sent_segment = sent.split(".")
    for i, s in enumerate(sent_segment):
        s = s.strip()
        sent_segment[i] = underthesea.word_tokenize(s, format="text").split()
    dump = [[i, 'O'] for s in sent_segment for i in s]
    dump_set = NerDataset(feature_for_phobert([dump], tokenizer=tokenizer_topic, use_crf=True))    
    dump_iter = DataLoader(dump_set, batch_size=1)
    with torch.no_grad():
        for idx, batch in enumerate(dump_iter):
            batch = { k:v.to(device) for k, v in batch.items() }        
            outputs = model_topic(**batch)
    pred_topic = list(set([topic_tags[i] for i in cvt2cls(outputs["tags"][0])]))
    return str({"sentiment": pred_sent, "topic": pred_topic})
    

def flip_image(x):
    return np.fliplr(x)


with gr.Blocks() as demo:
    gr.Markdown("Demo projects Review Company and Resume parser phase 1.")
    with gr.Tab("Review Company"):
        text_input = gr.Textbox(label="Input sentence:", placeholder="input here...")
        text_output = gr.Textbox(label="Result:")
        text_button = gr.Button("Predict")
    with gr.Tab("Extract infomation from resume"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Predict")

    # with gr.Accordion("Open for More!"):
    #     gr.Markdown("Look at me...")

    text_button.click(sentiment, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

demo.launch()