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import numpy as np
import pandas as pd
from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification
from scipy.special import softmax
import os
def check_csv(csv_file, data):
if os.path.isfile(csv_file):
data.to_csv(csv_file, mode='a', header=False, index=False, encoding='utf-8')
else:
history = data.copy()
history.to_csv(csv_file, index=False)
#Preprocess text
def preprocess(text):
new_text = []
for t in text.split(" "):
t = "@user" if t.startswith("@") and len(t) > 1 else t
t = "http" if t.startswith("http") else t
print(t)
new_text.append(t)
print(new_text)
return " ".join(new_text)
#Process the input and return prediction
def run_sentiment_analysis(text, tokenizer, model):
# save_text = {'tweet': text}
encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
output = model(**encoded_input)
scores_ = output[0][0].detach().numpy()
scores_ = softmax(scores_)
# Format output dict of scores
labels = ["Negative", "Neutral", "Positive"]
scores = {l:float(s) for (l,s) in zip(labels, scores_) }
# save_text.update(scores)
# user_data = {key: [value] for key,value in save_text.items()}
# data = pd.DataFrame(user_data,)
# check_csv('history.csv', data)
# hist_df = pd.read_csv('history.csv')
return scores
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