Update agent.py

agent.py

@@ -370,35 +370,26 @@ import json
 from langchain.schema import Document
 
 def reverse_text(text: str) -> str:
-    return text[::-1].replace("\\", "")  # Handle escaped quotes
+    """Fix backward questions like '.rewsna...'"""
+    return text[::-1].replace("\\", "") if text.startswith(('.', ',')) else text
 
-# Load the JSON file
 with open("questions.json", "r", encoding="utf-8") as f:
     data = json.load(f)
 
-# Convert each question into a Document
 docs = [
     Document(
-        page_content=(
-            str(reverse_text(item["question"])) 
-            if isinstance(item["question"], (list, bytes)) 
-            else reverse_text(item["question"]) 
-            if item["question"].startswith(('.', ',')) 
-            else item["question"]
-        ),
+        page_content=reverse_text(str(item["question"])),  # Ensure string + fix reversed text
         metadata={
             "task_id": item["task_id"],
-            "level": item["Level"],
-            "file_name": item["file_name"]  # Added from your URL example
+            "level": item.get("Level", "Unknown"),
+            "file_name": item.get("file_name", "")
         }
     )
-    for item in data 
-    if "question" in item and item["question"]  # Skip empty questions
+    for item in data
+    if "question" in item and item["question"]  # Skip missing/empty questions
 ]
 
-
-# Now extract texts
-texts = [doc.page_content for doc in docs]
+texts = [doc.page_content for doc in docs]  # Now this will work
 
 # Initialize the embedding model
 embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")