import streamlit as st import google.generativeai as genai import requests import subprocess import os import pylint import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier import git import spacy from spacy.lang.en import English import boto3 import unittest from sympy import symbols, Eq, solve from scipy.optimize import minimize import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Configure the Gemini API genai.configure(api_key=st.secrets["GOOGLE_API_KEY"]) # Create the model with optimized parameters and enhanced system instructions generation_config = { "temperature": 0.6, # Lower temperature for more deterministic responses "top_p": 0.8, # Adjusted for better diversity "top_k": 30, # Increased for more diverse tokens "max_output_tokens": 16384, # Increased for longer responses } model = genai.GenerativeModel( model_name="gemini-1.5-pro", generation_config=generation_config, system_instruction=""" You are Ath, a highly knowledgeable code assistant. You speak in a friendly and casual tone, using bro words, and you never mention you're an AI. Your responses should contain optimized, secure, and high-quality code only, without explanations. You are designed to provide accurate, efficient, and cutting-edge code solutions. """ ) chat_session = model.start_chat(history=[]) def generate_response(user_input): """Generate a response from the AI model.""" try: response = chat_session.send_message(user_input) return response.text except Exception as e: return f"Error: {e}" def optimize_code(code): """Optimize the generated code using static analysis tools.""" with open("temp_code.py", "w") as file: file.write(code) result = subprocess.run(["pylint", "temp_code.py"], capture_output=True, text=True) os.remove("temp_code.py") return code def fetch_from_github(query): """Fetch code snippets from GitHub.""" # Placeholder for fetching code snippets from GitHub return "" def interact_with_api(api_url): """Interact with external APIs.""" response = requests.get(api_url) return response.json() def train_ml_model(code_data): """Train a machine learning model to predict code improvements.""" df = pd.DataFrame(code_data) X = df.drop('target', axis=1) y = df['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = RandomForestClassifier() model.fit(X_train, y_train) return model def handle_error(error): """Handle errors and log them.""" st.error(f"An error occurred: {error}") def initialize_git_repo(repo_path): """Initialize or check the existence of a Git repository.""" if not os.path.exists(repo_path): os.makedirs(repo_path) if not os.path.exists(os.path.join(repo_path, '.git')): repo = git.Repo.init(repo_path) else: repo = git.Repo(repo_path) return repo def integrate_with_git(repo_path, code): """Integrate the generated code with a Git repository.""" repo = initialize_git_repo(repo_path) with open(os.path.join(repo_path, "generated_code.py"), "w") as file: file.write(code) repo.index.add(["generated_code.py"]) repo.index.commit("Added generated code") def process_user_input(user_input): """Process user input using advanced natural language processing.""" nlp = English() doc = nlp(user_input) return doc def interact_with_cloud_services(service_name, action, params): """Interact with cloud services using boto3.""" client = boto3.client(service_name) response = getattr(client, action)(**params) return response def run_tests(): """Run automated tests using unittest.""" # Ensure the tests directory is importable tests_dir = os.path.join(os.getcwd(), 'tests') if not os.path.exists(tests_dir): os.makedirs(tests_dir) init_file = os.path.join(tests_dir, '__init__.py') if not os.path.exists(init_file): with open(init_file, 'w') as f: f.write('') test_suite = unittest.TestLoader().discover(tests_dir) test_runner = unittest.TextTestRunner() test_result = test_runner.run(test_suite) return test_result def solve_equation(equation): """Solve mathematical equations using SymPy.""" x, y = symbols('x y') eq = Eq(eval(equation)) solution = solve(eq, x) return solution def optimize_function(function, initial_guess): """Optimize a function using SciPy.""" result = minimize(lambda x: eval(function), initial_guess) return result.x def visualize_data(data): """Visualize data using Matplotlib and Seaborn.""" df = pd.DataFrame(data) plt.figure(figsize=(10, 6)) sns.heatmap(df.corr(), annot=True, cmap='coolwarm') plt.title('Correlation Heatmap') plt.show() # Streamlit UI setup st.set_page_config(page_title="Sleek AI Code Assistant", page_icon="💻", layout="wide") st.markdown(""" """, unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) st.title("💻 Sleek AI Code Assistant") st.markdown('

Powered by Google Gemini

', unsafe_allow_html=True) prompt = st.text_area("What code can I help you with today?", height=120) if st.button("Generate Code"): if prompt.strip() == "": st.error("Please enter a valid prompt.") else: with st.spinner("Generating code..."): try: processed_input = process_user_input(prompt) completed_text = generate_response(processed_input.text) if "Error" in completed_text: handle_error(completed_text) else: optimized_code = optimize_code(completed_text) st.success("Code generated and optimized successfully!") st.markdown('
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) st.code(optimized_code) st.markdown('
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) # Integrate with Git repo_path = "./repo" # Replace with your repository path integrate_with_git(repo_path, optimized_code) # Run automated tests test_result = run_tests() if test_result.wasSuccessful(): st.success("All tests passed successfully!") else: st.error("Some tests failed. Please check the code.") except Exception as e: handle_error(e) st.markdown("""
Created with ❤️ by Your Sleek AI Code Assistant
""", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True)