Update app.py

app.py

@@ -7,35 +7,24 @@ import pandas as pd
 import numpy as np
 import os
 import time
+import re
+import json
 from datetime import datetime
+from azure.storage.fileshare import ShareServiceClient
 
 from utils import create_user_id
 
-from azure.storage.fileshare import ShareServiceClient
 
 
-import re
-import json
-print("1")
 # ClimateQ&A imports
 from climateqa.engine.llm import get_llm
-print("2")
-# from climateqa.chains import load_qa_chain_with_docs,load_qa_chain_with_text
-# from climateqa.chains import load_reformulation_chain
 from climateqa.engine.rag import make_rag_chain
-print("3")
 from climateqa.engine.vectorstore import get_pinecone_vectorstore
-print("4")
 from climateqa.engine.retriever import ClimateQARetriever
-print("5")
 from climateqa.engine.embeddings import get_embeddings_function
-print("6")
 from climateqa.engine.prompts import audience_prompts
-print("7")
 from climateqa.sample_questions import QUESTIONS
-print("8")
 from climateqa.constants import POSSIBLE_REPORTS
-print("9")
 from climateqa.utils import get_image_from_azure_blob_storage
 
 # Load environment variables in local mode
@@ -52,8 +41,6 @@ theme = gr.themes.Base(
     font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"],
 )
 
-print("1")
-
 
 
 init_prompt = ""
@@ -97,13 +84,6 @@ def parse_output_llm_with_sources(output):
     return content_parts
 
 
-
-# Create embeddings function and LLM
-print("1")
-embeddings_function = get_embeddings_function()
-print("1")
-
-
 # Create vectorstore and retriever
 vectorstore = get_pinecone_vectorstore(embeddings_function)