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#the below import has been replaced by the later mentioned import, recently by langchain as a per of their improvement strategy :)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
import openai
class ChatOpenAI:
def __init__(self, api_key, model_name="gpt-3.5-turbo", temperature=0.9):
self.api_key = api_key
self.model_name = model_name
self.temperature = temperature
openai.api_key = self.api_key
def chat(self, prompt):
response = openai.ChatCompletion.create(
model=self.model_name,
messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}],
temperature=self.temperature
)
return response['choices'][0]['message']['content']
#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:")
# Retrieve the API key from Hugging Face secret
api_key = os.getenv('OPENAI_API_KEY')
# 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(api_key,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)
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