Update app.py

app.py

@@ -5,10 +5,7 @@ from llama_index.core import StorageContext, load_index_from_storage, VectorStor
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from sentence_transformers import SentenceTransformer
-import firebase_admin
-from firebase_admin import db, credentials
 import datetime
-import uuid
 import random
 
 def select_random_name():
@@ -18,9 +15,7 @@ def select_random_name():
 # Example usage
 # Load environment variables
 load_dotenv()
-# authenticate to firebase
-cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
-firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
+
 # Configure the Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -44,7 +39,8 @@ os.makedirs(PERSIST_DIR, exist_ok=True)
 
 # Variable to store current chat conversation
 current_chat_history = []
-kkk=select_random_name()
+kkk = select_random_name()
+
 def data_ingestion_from_directory():
     # Use SimpleDirectoryReader on the directory containing the PDF files
     documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
@@ -96,10 +92,6 @@ print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
 # Define the function to handle predictions
-"""def predict(message,history):
-    response = handle_query(message)
-    return response"""
-
 def predict(message, history):
     logo_html = '''
     <div class="circle-logo">
@@ -109,30 +101,13 @@ 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>'
     return response_with_logo
-def save_chat_message(session_id, message_data):
-    ref = db.reference(f'/chat_history/{session_id}')  # Use the session ID to save chat data
-    ref.push().set(message_data)
 
 # Define your Gradio chat interface function (replace with your actual logic)
 def chat_interface(message, history):
     try:
-        # Generate a unique session ID for this chat session
-        session_id = str(uuid.uuid4())
-
         # Process the user message and generate a response (your chatbot logic)
         response = handle_query(message)
 
-        # Capture the message data
-        message_data = {
-            "sender": "user",
-            "message": message,
-            "response": response,
-            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
-        }
-
-        # Call the save function to store in Firebase with the generated session ID
-        save_chat_message(session_id, message_data)
-
         # Return the bot response
         return response
     except Exception as e:
@@ -166,12 +141,10 @@ footer {
 label.svelte-1b6s6s {display: none}
 div.svelte-rk35yg {display: none;}
 div.progress-text.svelte-z7cif2.meta-text {display: none;}
-
-
 '''
 
 gr.ChatInterface(chat_interface,
                  css=css,
                  description="Lily",
                  clear_btn=None, undo_btn=None, retry_btn=None,
-                 ).launch()
+                 ).launch()