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import torch
import gradio as gr
from model import SentimentAnalysisModel
# Load the pre-trained sentiment analysis model
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()
# Mapping from predicted class to emoji
emoji_to_emotion = {
0: 'joy 😆',
1: 'fear 😱',
2: 'anger 😡',
3: 'sadness 😭',
4: 'disgust 🤮',
5: 'shame 😳',
6: 'guilt 😞'
}
# Function to make predictions
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)
# Map predicted class to emoji
result = emoji_to_emotion[predicted_class.item()]
return result
# Create title, description and article strings
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."
# Interface for Gradio
iface = gr.Interface(
fn=predict_sentiment,
inputs="text",
outputs="text",
live=True,
theme="huggingface",
interpretation="default",
title=title,
description=description,
article=article)
# Launch the Gradio interface
iface.launch()
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