app.py

import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import warnings
from huggingface_hub import spaces

# Suppress all warnings
warnings.filterwarnings("ignore")

os.environ["TRANSFORMERS_CACHE"] = "/tmp"

# Initialize GPU for Hugging Face Spaces
@spaces.GPU
def init_gpu():
    return torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Initialize model and tokenizer
MODEL_NAME = "s-nlp/roberta-base-formality-ranker"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# Move model to GPU
device = init_gpu()
model = model.to(device)

app = FastAPI(title="Formality Classifier API")

class TextInput(BaseModel):
    text: str

def calculate_formality_percentages(score):
    # Convert score to grayscale percentage (0-100)
    grayscale = int(score * 100)
    # Use grayscale to determine formal/informal percentages
    formal_percent = grayscale
    informal_percent = 100 - grayscale
    return formal_percent, informal_percent

@app.get("/")
async def home():
    return {"message": "Formality Classifier API is running! Use /predict to classify text."}

@app.post("/predict")
async def predict_formality(input_data: TextInput):
    try:
        # Tokenize input
        encoding = tokenizer(input_data.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)
        
        # Create response in the new format
        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
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)