os import

app.py

@@ -1,72 +1,73 @@
-#the below import has been replaced by the later mentioned import, recently by langchain as a per of their improvement strategy :)
-#from langchain.chat_models import ChatOpenAI
-from langchain_openai import ChatOpenAI
-
-from langchain.schema import HumanMessage, SystemMessage
-from io import StringIO
-import streamlit as st
-from dotenv import load_dotenv
-import time
-import base64
-
-
-#This function is typically used in Python to load environment variables from a .env file into the application's environment.
-load_dotenv()
-
-st.title("Let's do code review for your python code")
-st.header("Please upload your .py file here:")
-
-
-# Function to download text content as a file using Streamlit
-def text_downloader(raw_text):
-    # Generate a timestamp for the filename to ensure uniqueness
-    timestr = time.strftime("%Y%m%d-%H%M%S")
-    
-    # Encode the raw text in base64 format for file download
-    b64 = base64.b64encode(raw_text.encode()).decode()
-    
-    # Create a new filename with a timestamp
-    new_filename = "code_review_analysis_file_{}_.txt".format(timestr)
-    
-    st.markdown("#### Download File ✅###")
-    
-    # Create an HTML link with the encoded content and filename for download
-    href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click Here!!</a>'
-    
-    # Display the HTML link using Streamlit markdown
-    st.markdown(href, unsafe_allow_html=True)
-
-# Capture the .py file data
-data = st.file_uploader("Upload python file",type=".py")
-
-if data:
-
-    # Create a StringIO object and initialize it with the decoded content of 'data'
-    stringio = StringIO(data.getvalue().decode('utf-8'))
-
-    # Read the content of the StringIO object and store it in the variable 'read_data'
-    fetched_data = stringio.read()
-
-    # Optionally, uncomment the following line to write the read data to the streamlit app
-    st.write(fetched_data)
-
-    # Initialize a ChatOpenAI instance with the specified model name "gpt-3.5-turbo" and a temperature of 0.9.
-    chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
-
-    # Create a SystemMessage instance with the specified content, providing information about the assistant's role.
-    systemMessage = SystemMessage(content="You are a code review assistant. Provide detailed suggestions to improve the given Python code along by mentioning the existing code line by line with proper indent")
-
-    # Create a HumanMessage instance with content read from some data source.
-    humanMessage = HumanMessage(content=fetched_data)
-
-    # Call the chat method of the ChatOpenAI instance, passing a list of messages containing the system and human messages.
-    # Recently langchain has recommended to use invoke function for the below please :)
-    finalResponse = chat.invoke([systemMessage, humanMessage])
-
-    
-    #Display review comments
-    st.markdown(finalResponse.content)
-
-
-    text_downloader(finalResponse.content)
-
+#the below import has been replaced by the later mentioned import, recently by langchain as a per of their improvement strategy :)
+#from langchain.chat_models import ChatOpenAI
+from langchain_openai import ChatOpenAI
+
+from langchain.schema import HumanMessage, SystemMessage
+from io import StringIO
+import streamlit as st
+from dotenv import load_dotenv
+import time
+import base64
+import os
+
+
+#This function is typically used in Python to load environment variables from a .env file into the application's environment.
+load_dotenv()
+
+st.title("Let's do code review for your python code")
+st.header("Please upload your .py file here:")
+
+
+# Function to download text content as a file using Streamlit
+def text_downloader(raw_text):
+    # Generate a timestamp for the filename to ensure uniqueness
+    timestr = time.strftime("%Y%m%d-%H%M%S")
+    
+    # Encode the raw text in base64 format for file download
+    b64 = base64.b64encode(raw_text.encode()).decode()
+    
+    # Create a new filename with a timestamp
+    new_filename = "code_review_analysis_file_{}_.txt".format(timestr)
+    
+    st.markdown("#### Download File ✅###")
+    
+    # Create an HTML link with the encoded content and filename for download
+    href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click Here!!</a>'
+    
+    # Display the HTML link using Streamlit markdown
+    st.markdown(href, unsafe_allow_html=True)
+
+# Capture the .py file data
+data = st.file_uploader("Upload python file",type=".py")
+
+if data:
+
+    # Create a StringIO object and initialize it with the decoded content of 'data'
+    stringio = StringIO(data.getvalue().decode('utf-8'))
+
+    # Read the content of the StringIO object and store it in the variable 'read_data'
+    fetched_data = stringio.read()
+
+    # Optionally, uncomment the following line to write the read data to the streamlit app
+    st.write(fetched_data)
+
+    # Initialize a ChatOpenAI instance with the specified model name "gpt-3.5-turbo" and a temperature of 0.9.
+    chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
+
+    # Create a SystemMessage instance with the specified content, providing information about the assistant's role.
+    systemMessage = SystemMessage(content="You are a code review assistant. Provide detailed suggestions to improve the given Python code along by mentioning the existing code line by line with proper indent")
+
+    # Create a HumanMessage instance with content read from some data source.
+    humanMessage = HumanMessage(content=fetched_data)
+
+    # Call the chat method of the ChatOpenAI instance, passing a list of messages containing the system and human messages.
+    # Recently langchain has recommended to use invoke function for the below please :)
+    finalResponse = chat.invoke([systemMessage, humanMessage])
+
+    
+    #Display review comments
+    st.markdown(finalResponse.content)
+
+
+    text_downloader(finalResponse.content)
+