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import torch
import gradio as gr
from model import SentimentAnalysisModel
from timeit import default_timer as timer
# 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_75.pth", map_location=torch.device('cpu')), strict=False)
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):
start_time = timer()
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
probabilities = torch.nn.functional.softmax(logits, dim=1)
# Map predicted class to emoji
predicted_class = torch.argmax(logits, dim=1).item()
result = emoji_to_emotion[predicted_class]
# Create a dictionary of class probabilities
class_probabilities = {emoji_to_emotion[i]: float(probabilities[0, i]) for i in range(len(emoji_to_emotion))}
# Calculate prediction time
pred_time = round(timer() - start_time, 5)
return class_probabilities, pred_time
# 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."
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=[gr.Label(num_top_classes=7, label="Predictions"),
gr.Number(label="Prediction time (s)")],
title=title,
description=description,
article=article)
# Launch the Gradio interface
iface.launch()
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