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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification | |
| from scipy.special import softmax | |
| import os | |
| # Requirements | |
| def load_distilbert(): | |
| model_path = "bright1/fine-tuned-distilbert-base-uncased" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| # config = AutoConfig.from_pretrained(model_path) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| return model, tokenizer | |
| def load_roberta(): | |
| model_path = "bright1/fine-tuned-twitter-Roberta-base-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| # config = AutoConfig.from_pretrained(model_path) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| return model, tokenizer | |
| # 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 sentiment_analysis(model_type, text): | |
| if model_type== 'distilbert': | |
| model, tokenizer = load_distilbert() | |
| else: | |
| model, tokenizer = load_roberta() | |
| save_text = {'tweet': text} | |
| text = preprocess(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 | |
| # , hist_df.head() | |
| model_type = gr.Radio(choices=['distilbert', 'roberta'], label='Select model type', value='roberta' ) | |
| #Gradio app interface | |
| #Gradio app interface | |
| demo = gr.Interface(fn = sentiment_analysis, | |
| inputs = [model_type, gr.TextArea("Write your text or tweet here", label="Analyze your COVID-19 tweets" )], | |
| outputs = ["label"], | |
| title = "COVID-19 Vaccine Tweet Analyzer App", | |
| description = "COVID-19 Tweets Analyzer", | |
| interpretation = "default", | |
| examples = [["roberta", "Being vaccinated is actually awesome :)"]] | |
| ).launch() | |