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import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp"
from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
app = Flask(__name__)
# Load the model and tokenizer
model_name = "s-nlp/roberta-base-formality-ranker"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Fuzzy classification function
def fuzzy_formality(score, threshold=0.75):
if score < threshold:
formal_weight = 0.5 * (score / threshold) ** 2
else:
formal_weight = 1 - 0.5 * ((1 - score) / (1 - threshold)) ** 2
informal_weight = 1 - formal_weight
formal_percent = round(formal_weight * 100)
informal_percent = round(informal_weight * 100)
return {
"formal_percent": formal_percent,
"informal_percent": informal_percent,
"classification": f"Your speech is {formal_percent}% formal and {informal_percent}% informal."
}
@app.route("/predict", methods=["POST"])
def predict_formality():
text = request.json.get("text")
if not text:
return jsonify({"error": "Text input is required"}), 400
# Tokenize input
encoding = tokenizer(
text, add_special_tokens=True, truncation=True, padding="max_length", return_tensors="pt"
)
# Get predictions
with torch.no_grad():
output = model(**encoding)
# Extract formality score
softmax_scores = output.logits.softmax(dim=1)
formality_score = softmax_scores[:, 1].item()
# Classify using fuzzy logic
result = fuzzy_formality(formality_score)
return jsonify({
"text": text,
"formality_score": round(formality_score, 3),
**result
})
# Ensure Flask runs on the correct port
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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