import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, BertModel, BertForSequenceClassification
from sklearn import metrics
import streamlit as st

# Have data for BertClass ready for our tuned model.
class BertClass(torch.nn.Module):
  def __init__(self):
    super(BertClass, self).__init__()
    self.l1 = BertModel.from_pretrained(model_path)
    self.dropout = torch.nn.Dropout(HEAD_DROP_OUT)
    self.classifier = torch.nn.Linear(768, 6)
    
  def forward(self, input_ids, attention_mask, token_type_ids):
    output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
    hidden_state = output_1[0]
    pooler = hidden_state[:, 0]
    pooler = self.dropout(pooler)
    output = self.classifier(pooler)
    return output

# Define models to be used
bert_path = "bert-base-uncased"
bert_tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = BertForSequenceClassification.from_pretrained(bert_path, num_labels=6)
tuned_model = model = torch.load("pytorch_bert_toxic.bin", map_location=torch.device("cpu"))

# Read and format data.
tweets_raw = pd.read_csv("test.csv", nrows=20)
labels_raw = pd.read_csv("test_labels.csv", nrows=20)

label_set = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
label_vector = labels_raw[label_set].values.tolist()

tweet_df = tweets_raw[["comment_text"]]
tweet_df["labels"] = label_vector

# User selects model for front-end.
option = st.selectbox("Select a text analysis model:", ("BERT", "Fine-tuned BERT"))
if option == "BERT":
    tokenizer = bert_tokenizer
    model = bert_model
else:
    tokenizer = bert_tokenizer
    model = tuned_model

# Dataset for loading tables into DataLoader
class ToxicityDataset(Dataset):
    def __init__(self, dataframe, tokenizer, max_len):
        self.tokenizer = tokenizer
        self.data = dataframe
        self.text = self.data.comment_text
        self.targets = self.data.labels
        self.max_len = max_len

    def __len__(self):
        return len(self.text)

    def __getitem__(self, index):
        text = str(self.text[index])
        text = " ".join(text.split())

        inputs = self.tokenizer.encode_plus(
            text,
            None,
            add_special_tokens=True,
            max_length=self.max_len,
            padding="max_length",
            truncation=True,
            return_token_type_ids=True,
        )
        ids = inputs["input_ids"]
        mask = inputs["attention_mask"]
        token_type_ids = inputs["token_type_ids"]
        return {
            "ids": torch.tensor(ids, dtype=torch.long),
            "mask": torch.tensor(mask, dtype=torch.long),
            "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
            "targets": torch.tensor(self.targets[index], dtype=torch.float),
        }

# Based on user model selection, prepare Dataset and DataLoader
MAX_LENGTH = 100
INFER_BATCH_SIZE = 128
infer_dataset = ToxicityDataset(tweet_df, tokenizer, MAX_LENGTH)
infer_params = {"batch_size": INFER_BATCH_SIZE, "shuffle": False}
infer_loader = DataLoader(infer_dataset, **test_params)

# Freeze model and input tokens
def inference():
    model.eval()
    final_targets = []
    final_outputs = []
    with torch.no_grad():
        for _, data in enumerate(infer_loader, 0):
            ids = data["ids"].to(device, dtype=torch.long)
            mask = data["mask"].to(device, dtype=torch.long)
            token_type_ids = data["token_type_ids"].to(device, dtype=torch.long)
            targets = data["targets"].to(device, dtype=torch.float)
            outputs = model(ids, mask, token_type_ids)
            final_targets.extend(targets.cpu().detach().numpy().tolist())
            final_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist())
    return final_outputs, final_targets

prediction, targets = inference()
prediction = np.array(prediction) >= 0.5
targets = np.argmax(targets, axis=1)
prediction = np.argmax(prediction, axis=1)
accuracy = metrics.accuracy_score(targets, prediction)
f1_score_micro = metrics.f1_score(targets, prediction, average="micro")
f1_score_macro = metrics.f1_score(targets, prediction, average="macro")

st.write(prediction)
st.write(f"Accuracy Score = {accuracy}")
st.write(f"F1 Score (Micro) = {f1_score_micro}")
st.write(f"F1 Score (Macro) = {f1_score_macro}")