|
|
|
import torch |
|
import gradio as gr |
|
from model import SentimentAnalysisModel |
|
|
|
|
|
model = SentimentAnalysisModel(bert_model_name="SamLowe/roberta-base-go_emotions", num_labels=7) |
|
model.load_state_dict(torch.load("best_model_0.75.pth", map_location=torch.device('cpu'))) |
|
model.eval() |
|
|
|
|
|
emoji_to_emotion = { |
|
0: 'joy ๐', |
|
1: 'fear ๐ฑ', |
|
2: 'anger ๐ก', |
|
3: 'sadness ๐ญ', |
|
4: 'disgust ๐คฎ', |
|
5: 'shame ๐ณ', |
|
6: 'guilt ๐' |
|
} |
|
|
|
|
|
def predict_sentiment(text): |
|
inputs = model.tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
|
input_ids = inputs["input_ids"] |
|
attention_mask = inputs["attention_mask"] |
|
|
|
with torch.no_grad(): |
|
outputs = model(input_ids=input_ids, attention_mask=attention_mask) |
|
|
|
logits = outputs.logits |
|
_, predicted_class = torch.max(logits, dim=1) |
|
|
|
|
|
result = emoji_to_emotion[predicted_class.item()] |
|
|
|
return result |
|
|
|
|
|
title = "Emoji-aware Sentiment Analysis using Roberta Model" |
|
description = "Explore the power of sentiment analysis with our Emotion Detector! Simply input a sentence or text, and let our model predict the underlying emotion. Discover the magic of AI in understanding human sentiments." |
|
article = "Sentiment Analysis, also known as opinion mining, is a branch of Natural Language Processing (NLP) that involves determining the emotional tone behind a piece of text. This powerful tool allows us to uncover the underlying feelings, attitudes, and opinions expressed in written communication." |
|
|
|
|
|
|
|
iface = gr.Interface( |
|
fn=predict_sentiment, |
|
inputs="text", |
|
outputs="text", |
|
live=True, |
|
theme="huggingface", |
|
interpretation="default", |
|
title=title, |
|
description=description, |
|
article=article) |
|
|
|
|
|
iface.launch() |
|
|