Update app.py

app.py

@@ -1,18 +1,14 @@
-from dotenv import load_dotenv
 import gradio as gr
 import os
+from dotenv import load_dotenv
 from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from sentence_transformers import SentenceTransformer
-import datetime
 import random
+import datetime
+import uuid
+import json
 
-def select_random_name():
-    names = ['Clara', 'Lily']
-    return random.choice(names)
-
-# Example usage
 # Load environment variables
 load_dotenv()
 
@@ -31,7 +27,7 @@ Settings.embed_model = HuggingFaceEmbedding(
 
 # Define the directory for persistent storage and data
 PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
+PDF_DIRECTORY = 'data'
 
 # Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
@@ -39,7 +35,7 @@ os.makedirs(PERSIST_DIR, exist_ok=True)
 
 # Variable to store current chat conversation
 current_chat_history = []
-kkk = select_random_name()
+kkk = random.choice(['Clara', 'Lily'])
 
 def data_ingestion_from_directory():
     # Use SimpleDirectoryReader on the directory containing the PDF files
@@ -87,9 +83,14 @@ def handle_query(query):
 
     return response
 
-# Example usage: Process PDF ingestion from directory
-print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
-data_ingestion_from_directory()
+def save_chat_history(history):
+    # Save the chat history to a local file or Firebase
+    session_id = str(uuid.uuid4())
+    chat_history_path = f"chat_history_{session_id}.json"
+    
+    with open(chat_history_path, 'w') as f:
+        json.dump(history, f)
+    print(f"Chat history saved as {chat_history_path}")
 
 # Define the function to handle predictions
 def predict(message, history):
@@ -100,14 +101,21 @@ def predict(message, history):
     '''
     response = handle_query(message)
     response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
+    
+    # Save the updated history
+    save_chat_history(current_chat_history)
+    
     return response_with_logo
 
 # Define your Gradio chat interface function (replace with your actual logic)
 def chat_interface(message, history):
     try:
-        # Process the user message and generate a response (your chatbot logic)
+        # Process the user message and generate a response
         response = handle_query(message)
 
+        # Update the history and save it
+        save_chat_history(current_chat_history)
+
         # Return the bot response
         return response
     except Exception as e: