Update app.py

app.py

@@ -4,7 +4,6 @@ import os
 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 firebase_admin
 from firebase_admin import db, credentials
 import datetime
@@ -15,13 +14,14 @@ def select_random_name():
     names = ['Clara', 'Lily']
     return random.choice(names)
 
-# Example usage
 # Load environment variables
 load_dotenv()
-# authenticate to firebase
+
+# 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
+
+# Configure Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="facebook/rag-token-nq",
     tokenizer_name="facebook/rag-token-nq",
@@ -34,9 +34,9 @@ Settings.embed_model = HuggingFaceEmbedding(
     model_name="BAAI/bge-small-en-v1.5"
 )
 
-# Define the directory for persistent storage and data
+# Define directories for storage and data
 PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
+PDF_DIRECTORY = 'data'  # Directory containing PDFs
 
 # Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
@@ -46,7 +46,7 @@ os.makedirs(PERSIST_DIR, exist_ok=True)
 current_chat_history = []
 
 def data_ingestion_from_directory():
-    # Use SimpleDirectoryReader on the directory containing the PDF files
+    # Use SimpleDirectoryReader on the directory containing PDF files
     documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
     storage_context = StorageContext.from_defaults()
     index = VectorStoreIndex.from_documents(documents)
@@ -91,14 +91,6 @@ 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()
-
-# 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">
@@ -108,17 +100,17 @@ 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 = db.reference(f'/chat_history/{session_id}')
     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)
+        # Process the user message and generate a response
         response = handle_query(message)
 
         # Capture the message data
@@ -126,13 +118,12 @@ def chat_interface(message, history):
             "sender": "user",
             "message": message,
             "response": response,
-            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
+            "timestamp": datetime.datetime.now().isoformat()
         }
 
-        # Call the save function to store in Firebase with the generated session ID
+        # Save the chat message to Firebase
         save_chat_message(session_id, message_data)
 
-        # Return the bot response
         return response
     except Exception as e:
         return str(e)
@@ -140,32 +131,34 @@ def chat_interface(message, history):
 # Custom CSS for styling
 css = '''
   .circle-logo {
-  display: inline-block;
-  width: 40px;
-  height: 40px;
-  border-radius: 50%;
-  overflow: hidden;
-  margin-right: 10px;
-  vertical-align: middle;
-}
-.circle-logo img {
-  width: 100%;
-  height: 100%;
-  object-fit: cover;
-}
-.response-with-logo {
-  display: flex;
-  align-items: center;
-  margin-bottom: 10px;
-}
-footer {
+    display: inline-block;
+    width: 40px;
+    height: 40px;
+    border-radius: 50%;
+    overflow: hidden;
+    margin-right: 10px;
+    vertical-align: middle;
+  }
+  .circle-logo img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+  }
+  .response-with-logo {
+    display: flex;
+    align-items: center;
+    margin-bottom: 10px;
+  }
+  footer {
     display: none !important;
     background-color: #F8D7DA;
   }
-label.svelte-1b6s6s {display: none}
+  label.svelte-1b6s6s {display: none}
 '''
-gr.ChatInterface(chat_interface,
-                 css=css,
-                 description="Clara",
-                 clear_btn=None, undo_btn=None, retry_btn=None,
-                 ).launch()
+
+gr.ChatInterface(
+    chat_interface,
+    css=css,
+    description="Clara",
+    clear_btn=None, undo_btn=None, retry_btn=None
+).launch()