Update app.py

app.py

@@ -25,10 +25,13 @@ Settings.embed_model = HuggingFaceEmbedding(
 PERSIST_DIR = "db"
 PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
 
-# Ensure PDF directory exists
+# Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
+# Variable to store current chat conversation
+current_chat_history = []
+
 def data_ingestion_from_directory():
     # Use SimpleDirectoryReader on the directory containing the PDF files
     documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
@@ -36,7 +39,7 @@ def data_ingestion_from_directory():
     index = VectorStoreIndex.from_documents(documents)
     index.storage_context.persist(persist_dir=PERSIST_DIR)
 
-def handle_query(query, chat_history):
+def handle_query(query):
     chat_text_qa_msgs = [
         (
             "user",
@@ -57,7 +60,7 @@ def handle_query(query, chat_history):
 
     # Use chat history to enhance response
     context_str = ""
-    for past_query, response in reversed(chat_history):
+    for past_query, response in reversed(current_chat_history):
         if past_query.strip():
             context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
 
@@ -71,10 +74,10 @@ def handle_query(query, chat_history):
     else:
         response = "Sorry, I couldn't find an answer."
 
-    return response
+    # Update current chat history
+    current_chat_history.append((query, response))
 
-# Initialize chat history
-chat_history = []
+    return response
 
 # Example usage: Process PDF ingestion from directory
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
@@ -88,15 +91,14 @@ input_component = gr.Textbox(
 
 output_component = gr.Textbox()
 
-# Function to add chat history to output
-def chat_with_history(query):
-    response = handle_query(query, chat_history)
-    chat_history.append((query, response))
+# Function to handle queries
+def chatbot_handler(query):
+    response = handle_query(query)
     return response
 
 # Create the Gradio interface
 interface = gr.Interface(
-    fn=chat_with_history,
+    fn=chatbot_handler,
     inputs=input_component,
     outputs=output_component,
     title="RedfernsTech Q&A Chatbot",