Update app.py

app.py

@@ -1,9 +1,9 @@
+import os
 import pandas as pd
 import torch
 from sentence_transformers import SentenceTransformer, util
 import faiss
 import numpy as np
-import os
 import pickle
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import scipy.special
@@ -18,6 +18,9 @@ import logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Disable tokenizers parallelism to avoid fork-related deadlocks
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
 # Paths for saving artifacts
 MODEL_DIR = "./saved_models"  # Primary location in /app/saved_models
 FALLBACK_MODEL_DIR = "/tmp/saved_models"  # Fallback if ./saved_models fails
@@ -167,6 +170,7 @@ def evaluate_response(args):
     penalty = min(1.0, max(0.5, skill_relevance))
     score *= penalty
 
+    logger.debug(f"Evaluated {skill}: score={score:.2f}, is_ai={is_ai_generated}")
     return skill, round(max(0, score), 2), is_ai_generated
 
 # Recommend Courses
@@ -299,6 +303,7 @@ def assess_skills():
             eval_args = [(skill, user_code, question) for skill, user_code, question in user_responses if user_code]
             logger.info(f"Evaluating {len(eval_args)} answers using multiprocessing pool.")
             results = pool.map(evaluate_response, eval_args)
+            logger.info(f"Evaluation results: {results}")
     except Exception as e:
         logger.error(f"Error in evaluate_response: {str(e)}", exc_info=True)
         return jsonify({"error": "Failed to evaluate answers due to an internal error."}), 500