Update app.py

app.py

@@ -3,14 +3,17 @@ import streamlit as st
 import PyPDF2
 import matplotlib.pyplot as plt
 from io import BytesIO
-from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
+from llama_index import VectorStoreIndex, SimpleDirectoryReader
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.llms.huggingface import HuggingFaceLLM
-import requests
+import dotenv
+
+# Load environment variables
+dotenv.load_dotenv()
 
 # Configure Hugging Face model
-Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
-Settings.llm = HuggingFaceLLM(model_name="sarvamai/sarvam-2b-v0.5", api_token=os.getenv("HUGGINGFACE_API_KEY"))
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+llm_model = HuggingFaceLLM(model_name="sarvamai/sarvam-2b-v0.5", api_token=os.getenv("HUGGINGFACE_API_KEY"))
 
 def write_to_file(content, filename="./files/test.pdf"):
     os.makedirs(os.path.dirname(filename), exist_ok=True)
@@ -23,11 +26,11 @@ def ingest_documents():
     return documents
 
 def load_data(documents):
-    index = VectorStoreIndex.from_documents(documents)
+    index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
     return index
 
 def generate_summary(index, document_text):
-    query_engine = index.as_query_engine()
+    query_engine = index.as_query_engine(llm_model=llm_model)
     response = query_engine.query(f"""
     You are a financial analyst. Your task is to provide a comprehensive summary of the given financial document.
     Analyze the following document and summarize it:
@@ -41,7 +44,7 @@ def generate_summary(index, document_text):
     5. Future outlook or forecasts
     6. Any notable financial risks or opportunities
     
-    Provide a clear, concise, and professional summary
+    Provide a clear, concise, and professional summary.
     """)
     return response.response