Modified openai keys and files

app.py

@@ -32,7 +32,7 @@ st.markdown("""
 """, unsafe_allow_html=True)
  
 # Title section
-st.markdown("<h1 class='title'>Manufacturing Process Analysis</h1>", unsafe_allow_html=True)
+st.markdown("<h1 class='title'>E-Commerce Analysis</h1>", unsafe_allow_html=True)
  
 with st.chat_message("assistant"):
     st.write("Hello 👋 How can I help you today?")

fakedatagenerator.ipynb

@@ -658,7 +658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "langenv",
    "language": "python",
    "name": "python3"
   },
@@ -672,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,

langchain_helper.py

@@ -1,12 +1,12 @@
 import os
-from langchain_openai import AzureOpenAI
+from langchain_openai import AzureOpenAI, OpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.agents.agent_types import AgentType
 from langchain_experimental.agents import create_pandas_dataframe_agent
 from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 from langchain.prompts import SemanticSimilarityExampleSelector
-from langchain_openai import AzureOpenAIEmbeddings
+from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain.prompts import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
@@ -19,16 +19,22 @@ import plotly
 import plotly.express as px
 from plotly.express import bar, line, scatter, area, pie
 
-from dotenv import load_dotenv
-load_dotenv()
+# from dotenv import load_dotenv
+# load_dotenv()
+
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv()) # read local .env file
+
+current_model_id = os.getenv('model_id')
 
 def get_few_shot_db_chain(user_message):
-    llm = AzureOpenAI(deployment_name="gpt-35-turbo-instruct", temperature=0.2)
+    #llm = AzureOpenAI(deployment_name="gpt-35-turbo-instruct", temperature=0.2)
+    llm = OpenAI(model = current_model_id)
     
     engine = create_engine("sqlite:///ecomm.db")
     db = SQLDatabase(engine=engine, sample_rows_in_table_info=3)
     
-    embeddings = AzureOpenAIEmbeddings(model="text-embedding-3-small")
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
     to_vectorize = [" ".join(example.values()) for example in few_shots]