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import gradio as gr
import torch
import numpy as np
from transformers import BertForSequenceClassification, BertTokenizer
import requests
import json
# Load model and tokenizer from Hugging Face Hub
repo_id = "logasanjeev/goemotions-bert"
model = BertForSequenceClassification.from_pretrained(repo_id)
tokenizer = BertTokenizer.from_pretrained(repo_id)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.eval()
# Load optimized thresholds from Hugging Face Hub
thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
response = requests.get(thresholds_url)
thresholds_data = json.loads(response.text)
emotion_labels = thresholds_data["emotion_labels"]
best_thresholds = thresholds_data["thresholds"]
# Prediction function
def predict_emotions(text):
encodings = tokenizer(
text,
padding='max_length',
truncation=True,
max_length=128,
return_tensors='pt'
)
input_ids = encodings['input_ids'].to(device)
attention_mask = encodings['attention_mask'].to(device)
with torch.no_grad():
outputs = model(input_ids, attention_mask=attention_mask)
logits = torch.sigmoid(outputs.logits).cpu().numpy()[0]
predictions = []
for i, (logit, thresh) in enumerate(zip(logits, best_thresholds)):
if logit >= thresh:
predictions.append((emotion_labels[i], logit))
predictions.sort(key=lambda x: x[1], reverse=True)
if not predictions:
return "No emotions predicted above thresholds."
return "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in predictions])
# Gradio interface
interface = gr.Interface(
fn=predict_emotions,
inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
outputs="text",
title="GoEmotions BERT Classifier",
description="Predict emotions using a fine-tuned BERT-base model from logasanjeev/goemotions-bert.",
examples=[
"I’m just chilling today.",
"Thank you for saving my life!",
"I’m nervous about my exam tomorrow."
]
)
if __name__ == "__main__":
interface.launch() |