Update app.py

app.py

@@ -4,12 +4,9 @@ import gradio as gr
 import qdrant_client
 from getpass import getpass
 
-# Set your OpenAI API key from environment variables.
+
 openai_api_key = os.getenv('OPENAI_API_KEY')
 
-# -------------------------------------------------------
-# Configure LlamaIndex with OpenAI LLM and Embeddings
-# -------------------------------------------------------
 from llama_index.llms.openai import OpenAI
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.core import Settings
@@ -17,14 +14,11 @@ from llama_index.core import Settings
 Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
 Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
 
-# -------------------------------------------------------
-# Import document readers, index, vector store, memory, etc.
-# -------------------------------------------------------
 from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
 from llama_index.vector_stores.qdrant import QdrantVectorStore
 from llama_index.core.memory import ChatMemoryBuffer
 
-# Global variables to hold the index and chat engine.
+
 chat_engine = None
 index = None
 query_engine = None
@@ -33,31 +27,21 @@ client = None
 vector_store = None
 storage_context = None
 
-# -------------------------------------------------------
-# Function to process uploaded files and build the index.
-# -------------------------------------------------------
+
 def process_upload(files):
-    """
-    Accepts a list of uploaded file paths, saves them to a local folder,
-    loads them as documents, and builds the vector index and chat engine.
-    This version accumulates files, so if you upload more files later,
-    they are added to the existing document set.
-    """
+
     upload_dir = "uploaded_files"
     if not os.path.exists(upload_dir):
         os.makedirs(upload_dir)
     
-    # Copy new files into the folder without clearing existing ones.
     for file_path in files:
         file_name = os.path.basename(file_path)
         dest = os.path.join(upload_dir, file_name)
         if not os.path.exists(dest):
             shutil.copy(file_path, dest)
     
-    # Load documents from the saved folder.
     documents = SimpleDirectoryReader(upload_dir).load_data()
     
-    # Build the index and chat engine using Qdrant as the vector store.
     global client, vector_store, storage_context, index, query_engine, memory, chat_engine
     client = qdrant_client.QdrantClient(location=":memory:")
     
@@ -80,19 +64,15 @@ def process_upload(files):
         chat_mode="context",
         memory=memory,
         system_prompt=(
-            "You are an AI assistant who answers the user questions, "
-            "use the schema fields to generate appropriate and valid json queries"
+            "You are an AI assistant who answers the user questions"
         ),
     )
     
     return "Documents uploaded and index built successfully!"
 
-# -------------------------------------------------------
-# Chat function that uses the built chat engine.
-# -------------------------------------------------------
+
 def chat_with_ai(user_input, chat_history):
     global chat_engine
-    # Check if the chat engine is initialized.
     if chat_engine is None:
         return chat_history, "Please upload documents first."
     
@@ -100,7 +80,6 @@ def chat_with_ai(user_input, chat_history):
     references = response.source_nodes
     ref, pages = [], []
     
-    # Extract file names from the source nodes (if available)
     for node in references:
         file_name = node.metadata.get('file_name')
         if file_name and file_name not in ref:
@@ -113,20 +92,15 @@ def chat_with_ai(user_input, chat_history):
         chat_history.append((user_input, str(response)))
     return chat_history, ""
 
-# -------------------------------------------------------
-# Function to clear the chat history.
-# -------------------------------------------------------
+
 def clear_history():
     return [], ""
 
-# -------------------------------------------------------
-# Build the Gradio interface.
-# -------------------------------------------------------
+
 def gradio_interface():
     with gr.Blocks() as demo:
-        gr.Markdown("# Chat Interface for LlamaIndex with File Upload")
+        gr.Markdown("# AI Assistant")
         
-        # Use Tabs to separate the file upload and chat interfaces.
         with gr.Tab("Upload Documents"):
             gr.Markdown("Upload PDF, Excel, CSV, DOC/DOCX, or TXT files below:")
             # The file upload widget: we specify allowed file types.
@@ -142,12 +116,12 @@ def gradio_interface():
             upload_button.click(process_upload, inputs=file_upload, outputs=upload_status)
         
         with gr.Tab("Chat"):
-            chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
+            chatbot = gr.Chatbot(label="Chatbot Assistant")
             user_input = gr.Textbox(
                 placeholder="Ask a question...", label="Enter your question"
             )
             submit_button = gr.Button("Send")
-            btn_clear = gr.Button("Clear History")
+            btn_clear = gr.Button("Restart")
             
             # A State to hold the chat history.
             chat_history = gr.State([])