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import os | |
import streamlit as st | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
from transformers import pipeline | |
os.system("pip freeze > requirements.txt") | |
os.system("pip install -r requirements.txt") | |
# Function to reverse engineer code | |
def reverse_prompt_engineer(input_code): | |
# Analyze the input code using langchain | |
analyzed_code = input_code | |
# Load the tokenizer and model | |
tokenizer = pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B") | |
model = tokenizer.model | |
# Generate a prompt using the analyzed code | |
prompt = "Reverse engineer the following code:\n\n" + analyzed_code | |
# Generate similar code using the ChatGPT agent's generate_code method | |
generated_code = tokenizer(prompt, max_length=100, do_sample=True) | |
return generated_code[0]['generated_text'] | |
# Set Streamlit page configuration | |
st.set_page_config( | |
page_title="Code Generator", | |
layout="wide", | |
initial_sidebar_state="expanded" | |
) | |
# Code Generation Page | |
st.title("Code Generator") | |
# User input code area | |
user_input = st.text_area("Input Code") | |
# Generate Code button | |
if st.button("Generate Code"): | |
if not user_input: | |
st.error("Input field is empty!") | |
else: | |
try: | |
generated_code = reverse_prompt_engineer(user_input) | |
st.code(generated_code) | |
except Exception as e: | |
st.error(f"An error occurred: {str(e)}") |