Create app.py

app.py

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+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+app = Flask(__name__)
+
+# Load the pretrained 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():
+    # Get input text from request
+    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()  # Extract formal score
+
+    # Classify using fuzzy logic
+    result = fuzzy_formality(formality_score)
+
+    return jsonify({
+        "text": text,
+        "formality_score": round(formality_score, 3),
+        **result
+    })
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)