Update app.py

app.py

@@ -1,10 +1,10 @@
-import os
-
-os.environ["TRANSFORMERS_CACHE"] = "/tmp"
-
 from flask import Flask, request, jsonify
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import os
+
+# Set writable cache
+os.environ["TRANSFORMERS_CACHE"] = "/tmp"
 
 app = Flask(__name__)
 
@@ -32,18 +32,13 @@ def fuzzy_formality(score, threshold=0.75):
 
 @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"
+        text, add_special_tokens=True, truncation=True, padding="max_length", return_tensors="pt"
     )
 
     # Get predictions
@@ -52,7 +47,7 @@ def predict_formality():
 
     # Extract formality score
     softmax_scores = output.logits.softmax(dim=1)
-    formality_score = softmax_scores[:, 1].item()  # Extract formal score
+    formality_score = softmax_scores[:, 1].item()
 
     # Classify using fuzzy logic
     result = fuzzy_formality(formality_score)
@@ -63,5 +58,6 @@ def predict_formality():
         **result
     })
 
+# Run on correct port
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)