Update app.py

app.py

@@ -1,16 +1,20 @@
 from flask import Flask, request, jsonify
 import torch
+import os
 from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
 
 app = Flask(__name__)
 
-MODEL_PATH = "./model"  # local folder with all model + tokenizer files
+# Hugging Face repo and token
+REPO = "aaronmrls/distilBERT-maintenance-priority-scorer"
+HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")  # optional (only needed if repo is private)
 
-print("Loading tokenizer and model from local path:", MODEL_PATH)
+print("Loading tokenizer and model from Hugging Face Hub...")
+print("Using Hugging Face token:", "Yes" if HF_TOKEN else "No")
 
-# Load tokenizer & model from local folder
-tokenizer = DistilBertTokenizerFast.from_pretrained(MODEL_PATH)
-model = DistilBertForSequenceClassification.from_pretrained(MODEL_PATH)
+# Load tokenizer & model directly from Hub
+tokenizer = DistilBertTokenizerFast.from_pretrained(REPO, use_auth_token=HF_TOKEN)
+model = DistilBertForSequenceClassification.from_pretrained(REPO, use_auth_token=HF_TOKEN)
 model.eval()
 
 @app.route("/predict", methods=["POST"])
@@ -20,6 +24,7 @@ def predict():
         results = []
 
         for item in data:
+            # Build input text (you can adjust formatting if needed)
             input_text = f"{item['category']} - {item['subcategory']} in {item['area']}. {item.get('comments', '')}"
             inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
 
@@ -30,6 +35,7 @@ def predict():
             results.append({"priority_score": predicted_class})
 
         return jsonify(results)
+
     except Exception as e:
         return jsonify({"error": str(e)}), 500