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import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import warnings
# Suppress NVML warning
warnings.filterwarnings("ignore", message="Can't initialize NVML")
os.environ["TRANSFORMERS_CACHE"] = "/tmp"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
MODEL_NAME = "s-nlp/roberta-base-formality-ranker"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model = model.to(device)
def calculate_formality_percentages(score):
grayscale = int(score * 100)
formal_percent = grayscale
informal_percent = 100 - grayscale
return formal_percent, informal_percent
def predict_formality(text):
# Tokenize input
encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
encoding = {k: v.to(device) for k, v in encoding.items()}
# Predict formality score
with torch.no_grad():
logits = model(**encoding).logits
score = logits.softmax(dim=1)[:, 1].item()
# Calculate percentages using grayscale
formal_percent, informal_percent = calculate_formality_percentages(score)
response = {
"formality_score": round(score, 3),
"formal_percent": formal_percent,
"informal_percent": informal_percent,
"classification": f"Your speech is {formal_percent}% formal and {informal_percent}% informal."
}
return response
demo = gr.Interface(
fn=predict_formality,
inputs=gr.Textbox(label="Enter your text", lines=3),
outputs=gr.JSON(label="Formality Analysis"),
title="Formality Classifier",
description="Enter text to analyze its formality level.",
examples=[
["Hello, how are you doing today?"],
["Hey, what's up?"],
["I would like to request your assistance with this matter."]
]
)
# Launch the app
if __name__ == "__main__":
demo.queue()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
) |