import gradio as gr
import pandas as pd
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
# summary function - test for single gradio function interface
# summary function - test for single gradio function interfrace
def bulk_function(filename):
  # Create class for data preparation
  class SimpleDataset:
      def __init__(self, tokenized_texts):
          self.tokenized_texts = tokenized_texts
      
      def __len__(self):
          return len(self.tokenized_texts["input_ids"])
      
      def __getitem__(self, idx):
          return {k: v[idx] for k, v in self.tokenized_texts.items()}

  # load tokenizer and model, create trainer
  model_name = "j-hartmann/emotion-english-distilroberta-base"
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForSequenceClassification.from_pretrained(model_name)
  trainer = Trainer(model=model)  
  print(filename, type(filename))
  print(filename.name)



  # read file lines
  with open(filename.name, "r") as f:
    lines = f.readlines()
  # expects unnamed:0 or index, col name -> strip both
  lines_s = [item.split("\n")[0].split(",")[-1] for item in lines]
  print(lines_s)
  print(filename)
 

    # Tokenize texts and create prediction data set
  tokenized_texts = tokenizer(lines_s,truncation=True,padding=True)
  pred_dataset = SimpleDataset(tokenized_texts)

    # Run predictions -> predict whole df
  predictions = trainer.predict(pred_dataset)

    # Transform predictions to labels
  preds = predictions.predictions.argmax(-1)
  labels = pd.Series(preds).map(model.config.id2label)
  scores = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True)).max(1)
    # scores raw
  temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True))

    # work in progress
  # container
  anger = []
  disgust = []
  fear = []
  joy = []
  neutral = []
  sadness = []
  surprise = []

  # extract scores (as many entries as exist in pred_texts)
  for i in range(len(lines_s)):
    anger.append(temp[i][0])
    disgust.append(temp[i][1])
    fear.append(temp[i][2])
    joy.append(temp[i][3])
    neutral.append(temp[i][4])
    sadness.append(temp[i][5])
    surprise.append(temp[i][6])

  # define df
  df = pd.DataFrame(list(zip(lines_s,preds,labels,scores,  anger, disgust, fear, joy, neutral, sadness, surprise)), columns=['text','pred','label','score', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])

  # save results to csv
  YOUR_FILENAME = filename.name.split(".")[0] + "_emotion_predictions" + ".csv"  # name your output file
  df.to_csv(YOUR_FILENAME)

  # return dataframe for space output
  return df