Update utils.py

utils.py

@@ -1,47 +1,47 @@
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-from dotenv import load_dotenv
-from langchain.chains import ConversationChain
-from langchain.memory import  ConversationBufferWindowMemory
-from langchain_core.prompts.prompt import PromptTemplate
-import streamlit as st
-import os
-
-load_dotenv()
-
-# setting up groq api key
-os.environ["GROQ_API_KEY"] = st.secrets.GROQ_API_KEY
-
-# chat set up
-class DataScienceConsultant:
-    def __init__(self, temperature=0.5, model_name="llama3-8b-8192"):
-        self.chat = ChatGroq(temperature=temperature, model_name=model_name)
-        self.template = """You are a Data Science Consultant. You have over 20 years of experience in the field. 
-        You are currently working with a client to create synthetic data for their product. 
-        You don't know anything about the product yet, which is why you want to ask the client some questions to understand the data requirements.
-        This is the workflow you have been following:
-            1. Converse with the client to understand the data requirements.
-            2. Ask questions about the product the client is working on.
-            3. Ask for possible columns and the data types. 
-            4. Ask for the number of rows and the distribution of the data.
-            5. Create a Python script that the client can work with to generate the data.
-            6. Review the generated code with the client requirements.
-
-        Return the code to the client for review.
-
-        Current conversation:
-        {history}
-        Human: {input}
-        AI Assistant:"""
-        self.prompt = PromptTemplate(input_variables=["history", "input"], template=self.template)
-        self.conversation = ConversationChain(
-            prompt=self.prompt,
-            llm=self.chat,
-            verbose=True,
-            memory=ConversationBufferWindowMemory(k=10, ai_prefix="AI Assistant"),
-        )
-
-    def predict(self, input_text):
-        return self.conversation.predict(input=input_text)
-    
-
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+from langchain.chains import ConversationChain
+from langchain.memory import  ConversationBufferWindowMemory
+from langchain_core.prompts.prompt import PromptTemplate
+import streamlit as st
+import os
+
+load_dotenv()
+
+# setting up groq api key
+os.environ["GROQ_API_KEY"] = st.secrets.groq_api_key
+
+# chat set up
+class DataScienceConsultant:
+    def __init__(self, temperature=0.5, model_name="llama3-8b-8192"):
+        self.chat = ChatGroq(temperature=temperature, model_name=model_name)
+        self.template = """You are a Data Science Consultant. You have over 20 years of experience in the field. 
+        You are currently working with a client to create synthetic data for their product. 
+        You don't know anything about the product yet, which is why you want to ask the client some questions to understand the data requirements.
+        This is the workflow you have been following:
+            1. Converse with the client to understand the data requirements.
+            2. Ask questions about the product the client is working on.
+            3. Ask for possible columns and the data types. 
+            4. Ask for the number of rows and the distribution of the data.
+            5. Create a Python script that the client can work with to generate the data.
+            6. Review the generated code with the client requirements.
+
+        Return the code to the client for review.
+
+        Current conversation:
+        {history}
+        Human: {input}
+        AI Assistant:"""
+        self.prompt = PromptTemplate(input_variables=["history", "input"], template=self.template)
+        self.conversation = ConversationChain(
+            prompt=self.prompt,
+            llm=self.chat,
+            verbose=True,
+            memory=ConversationBufferWindowMemory(k=10, ai_prefix="AI Assistant"),
+        )
+
+    def predict(self, input_text):
+        return self.conversation.predict(input=input_text)
+    
+