app.py

import os
from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

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

# Initialize
app = Flask(__name__)

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

@app.route("/", methods=["GET"])
def home():
    return jsonify({"message": "Formality Classifier API is running! Use /predict to classify text."})

@app.route("/predict", methods=["POST"])
def predict_formality():
    data = request.get_json()
    if not data or "text" not in data:
        return jsonify({"error": "Text input is required"}), 400

    text = data["text"]

    # Tokenize input
    encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

    # Predict formality score
    with torch.no_grad():
        logits = model(**encoding).logits
    score = logits.softmax(dim=1)[:, 1].item()

    # Convert score to formality classification
    formal_percent = round(score * 100)
    informal_percent = 100 - formal_percent
    classification = f"Your speech is {formal_percent}% formal and {informal_percent}% informal."

    return jsonify({
        "text": text,
        "formality_score": round(score, 3),
        "formal_percent": formal_percent,
        "informal_percent": informal_percent,
        "classification": classification
    })

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