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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Prasadrao/xlm-roberta-large-go-emotions-v3")
model = AutoModelForSequenceClassification.from_pretrained("Prasadrao/xlm-roberta-large-go-emotions-v3")
emotion_labels = ["admiration", "amusement", "anger", "annoyance", "approval",
"caring", "confusion", "curiosity", "desire", "disappointment",
"disapproval", "disgust", "embarrassment", "excitement",
"fear", "gratitude", "grief", "joy", "love", "nervousness",
"optimism", "pride", "realization", "relief", "remorse",
"sadness", "surprise", "neutral"]
def get_sentiment_emoji(sentiment):
# Define the emojis corresponding to each sentiment
emoji_mapping = {
"disappointment": "😞",
"sadness": "😢",
"annoyance": "😠",
"neutral": "😐",
"disapproval": "👎",
"realization": "😮",
"nervousness": "😬",
"approval": "👍",
"joy": "😄",
"anger": "😡",
"embarrassment": "😳",
"caring": "🤗",
"remorse": "😔",
"disgust": "🤢",
"grief": "😥",
"confusion": "😕",
"relief": "😌",
"desire": "😍",
"admiration": "😌",
"optimism": "😊",
"fear": "😨",
"love": "❤️",
"excitement": "🎉",
"curiosity": "🤔",
"amusement": "😄",
"surprise": "😲",
"gratitude": "🙏",
"pride": "🦁"
}
return emoji_mapping.get(sentiment, "")
import torch
import torch.nn.functional as F
def predict_emotion(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()
predicted_emotion = emotion_labels[predicted_class]
emoji = get_sentiment_emoji(predicted_emotion)
predicted_emotion = f" {predicted_emotion} {emoji}"
# Calculate softmax to get the probabilities
probabilities = F.softmax(logits, dim=1)
# Extract the score for the predicted class
predicted_score = round(probabilities[0, predicted_class].item()*100)
predicted_score = f"{predicted_score}%"
return predicted_emotion, predicted_score
import torch
import os
import gradio as gr
import random
#server_port = random.randint(1000, 9000)
title = "Emotion Detective: Analyzing Textual Sentiments"
description = "Explore the power of sentiment analysis with our Emotion Detective! 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"
examples = [["I feel ecstatic about winning the competition."],
["The news of her promotion made me feel proud and happy."],
["He felt devastated after hearing about the loss of his pet."],
["It's like I went through a rollercoaster of feelings!"],
["The memory of his actions haunted him, filling him with a profound sense of sorrow."],
["Despite the challenges ahead, he couldn't shake the feeling that good things were on the horizon"],
["The repugnant smell wafting from the trash bin made her nose crinkle in discomfort"],
["As he looked at his team's success, a feeling of triumph and contentment filled his heart."]]
demo = gr.Interface(
fn=predict_emotion,
inputs=gr.Textbox(lines=3,placeholder="Enter your text here.....",label="Text"),
outputs=[gr.Textbox(label="Emotion"), gr.Textbox(label="Score")],
title=title,
description=description,
flagging_options = ["True","False","Not Sure"],
examples = examples,
)
# Launch the Gradio interface
demo.launch(share=True)
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