Update app.py

app.py

@@ -7,10 +7,18 @@ from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from sentence_transformers import SentenceTransformer
 import csv
 import os
+import csv
+import os
+from datasets import Dataset, DatasetDict
 
 PERSIST_DIR = "history"  # Replace with your actual directory path
 CSV_FILE = os.path.join(PERSIST_DIR, "chat_history.csv")
 
+# Assuming current_chat_history is managed within a Dataset or DatasetDict
+current_chat_history = Dataset({"query": [], "response": []})
+
+
+
 # Load environment variables
 load_dotenv()
 
@@ -46,6 +54,8 @@ def data_ingestion_from_directory():
     index.storage_context.persist(persist_dir=PERSIST_DIR)
 
 def handle_query(query):
+    global current_chat_history
+
     # Ensure the directory exists or create it
     os.makedirs(PERSIST_DIR, exist_ok=True)
 
@@ -68,9 +78,9 @@ def handle_query(query):
     storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
     index = load_index_from_storage(storage_context)
 
-    # Use chat history to enhance response (assuming current_chat_history is defined)
+    # Use chat history to enhance response
     context_str = ""
-    for past_query, response in reversed(current_chat_history):
+    for past_query, response in reversed(current_chat_history["query"]):
         if past_query.strip():
             context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
 
@@ -85,7 +95,8 @@ def handle_query(query):
         response = "Sorry, I couldn't find an answer."
 
     # Update current chat history
-    current_chat_history.append((query, response))
+    current_chat_history["query"].append(query)
+    current_chat_history["response"].append(response)
 
     # Save chat history to CSV
     with open(CSV_FILE, 'a', newline='', encoding='utf-8') as file: