Update app.py

app.py

@@ -1,33 +1,32 @@
 #pip install llama-index-embeddings-huggingface
 import gradio as gr
 import os
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-# Access from Hugging Face secret
-openai_api_key = os.environ.get("OPENAI_API_KEY")
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.embeddings import HuggingFaceEmbedding
 
-if openai_api_key:
-    os.environ["OPENAI_API_KEY"] = openai_api_key  # optional if your library reads it directly
-else:
+# ✅ Access OpenAI API Key
+openai_api_key = os.environ.get("OPENAI_API_KEY")
+if not openai_api_key:
     raise ValueError("❌ OPENAI_API_KEY not found. Add it in Space settings > Secrets.")
+os.environ["OPENAI_API_KEY"] = openai_api_key
 
-# ✅ Use Hugging Face embedding model
+# ✅ Set Hugging Face Embedding globally via Settings
 embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-service_context = ServiceContext.from_defaults(embed_model=embed_model)
+Settings.embed_model = embed_model  # Replaces deprecated ServiceContext
 
 # ✅ Helper to load and filter documents
 def load_filtered_docs(folder):
     docs = SimpleDirectoryReader(folder).load_data()
     return [doc for doc in docs if doc.text and doc.text.strip()]
 
-# ✅ Load and index Paul Graham documents
+# ✅ Load Paul Graham documents
 pg_docs = load_filtered_docs("data/paul")
-pg_index = VectorStoreIndex.from_documents(pg_docs, service_context=service_context)
+pg_index = VectorStoreIndex.from_documents(pg_docs)
 pg_engine = pg_index.as_query_engine()
 
-# ✅ Load and index Insurance documents (PDF included)
+# ✅ Load Insurance documents
 ins_docs = load_filtered_docs("data/insurance")
-ins_index = VectorStoreIndex.from_documents(ins_docs, service_context=service_context)
+ins_index = VectorStoreIndex.from_documents(ins_docs)
 ins_engine = ins_index.as_query_engine()
 
 # ✅ Query functions
@@ -86,7 +85,6 @@ def launch_interface():
         """)
 
         with gr.Tabs():
-            # Paul Graham Tab
             with gr.Tab("Paul Graham"):
                 if os.path.exists("data/logo.png"):
                     gr.Image("data/logo.png", show_label=False, container=False, height=120)
@@ -109,7 +107,6 @@ def launch_interface():
                 submit_pg.click(query_pg, inputs=textbox_pg, outputs=output_pg)
                 clear_pg.click(lambda: ("", "", ""), outputs=[dropdown_pg, textbox_pg, output_pg])
 
-            # Insurance Tab
             with gr.Tab("Insurance"):
                 gr.Markdown("""
                     <div id='header-text'>