import torch
from utils import label_full_decoder
import sys
import dataset
import engine
from model import BERTBaseUncased
# from tokenizer import tokenizer
import config
from transformers import pipeline, AutoTokenizer, AutoModel
import gradio as gr

# DEVICE = config.device

import requests
URL = "https://huggingface.co/thak123/bert-emoji-latvian-twitter-classifier/resolve/main/model.bin"
response = requests.get(URL)
open("model.bin", "wb").write(response.content)

model_path = "pytorch_model.bin"

# model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
# 7 EPOCH Version
# BERT_PATH = "FFZG-cleopatra/bert-emoji-latvian-twitter"

# tokenizer = transformers.BertTokenizer.from_pretrained(
#     BERT_PATH,
#     do_lower_case=True
# )
#AutoTokenizer.from_pretrained("FFZG-cleopatra/bert-emoji-latvian-twitter")

# classifier = pipeline("sentiment-analysis",
#                       model= model, 
#                       tokenizer = tokenizer)

# MODEL = BERTBaseUncased()
# MODEL.load_state_dict(torch.load(config.MODEL_PATH, map_location=torch.device(DEVICE)))
# MODEL.eval()



# T = tokenizer.TweetTokenizer(
#     preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)

def preprocess(text):
    tokens = T.tokenize(text)
    print(tokens, file=sys.stderr)
    ptokens = []
    for index, token in enumerate(tokens):
        if "@" in token:
            if index > 0:
                # check if previous token was mention
                if "@" in tokens[index-1]:
                    pass
                else:
                    ptokens.append("mention_0")
            else:
                ptokens.append("mention_0")
        else:
            ptokens.append(token)

    print(ptokens, file=sys.stderr)
    return " ".join(ptokens)


def sentence_prediction(sentence):
    # sentence = preprocess(sentence)
    
    # model_path = config.MODEL_PATH

    # test_dataset = dataset.BERTDataset(
    #     review=[sentence],
    #     target=[0]
    # )

    # test_data_loader = torch.utils.data.DataLoader(
    #     test_dataset,
    #     batch_size=config.VALID_BATCH_SIZE,
    #     num_workers=3
    # )

    # device = config.device

    model = BERTBaseUncased()
    model.load_state_dict(torch.load(
        model_path, map_location=torch.device(device)))
    model.to(device)

    outputs, [] = engine.predict_fn(test_data_loader, MODEL, device)

    outputs =  classifier(sentence)
    
    print(outputs)
    return outputs #{"label":outputs[0]}




demo = gr.Interface(
  fn=sentence_prediction, 
  inputs='text',
  outputs='label',
)

demo.launch()