app.py

import streamlit as st
import google.generativeai as genai
import requests
import subprocess
import os
import pylint.epylint as lint
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# 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):
    try:
        response = chat_session.send_message(user_input)
        return response.text
    except Exception as e:
        return f"Error: {e}"

def optimize_code(code):
    # Placeholder for advanced code optimization logic
    # This could involve using external tools or libraries for static analysis and optimization
    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
    return code

def fetch_from_github(query):
    # Placeholder for fetching code snippets from GitHub
    # This could involve using the GitHub API to search for relevant code
    return ""

def interact_with_api(api_url):
    # Placeholder for interacting with external APIs
    response = requests.get(api_url)
    return response.json()

def train_ml_model(code_data):
    # Placeholder for training 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

# Streamlit UI setup
st.set_page_config(page_title="Sleek AI Code Assistant", page_icon="💻", layout="wide")

st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
    
    body {
        font-family: 'Inter', sans-serif;
        background-color: #f0f4f8;
        color: #1a202c;
    }
    .stApp {
        max-width: 1000px;
        margin: 0 auto;
        padding: 2rem;
    }
    .main-container {
        background: #ffffff;
        border-radius: 16px;
        padding: 2rem;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
    }
    h1 {
        font-size: 2.5rem;
        font-weight: 700;
        color: #2d3748;
        text-align: center;
        margin-bottom: 1rem;
    }
    .subtitle {
        font-size: 1.1rem;
        text-align: center;
        color: #4a5568;
        margin-bottom: 2rem;
    }
    .stTextArea textarea {
        border: 2px solid #e2e8f0;
        border-radius: 8px;
        font-size: 1rem;
        padding: 0.75rem;
        transition: all 0.3s ease;
    }
    .stTextArea textarea:focus {
        border-color: #4299e1;
        box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
    }
    .stButton button {
        background-color: #4299e1;
        color: white;
        border: none;
        border-radius: 8px;
        font-size: 1.1rem;
        font-weight: 600;
        padding: 0.75rem 2rem;
        transition: all 0.3s ease;
        width: 100%;
    }
    .stButton button:hover {
        background-color: #3182ce;
    }
    .output-container {
        background: #f7fafc;
        border-radius: 8px;
        padding: 1rem;
        margin-top: 2rem;
    }
    .code-block {
        background-color: #2d3748;
        color: #e2e8f0;
        font-family: 'Fira Code', monospace;
        font-size: 0.9rem;
        border-radius: 8px;
        padding: 1rem;
        margin-top: 1rem;
        overflow-x: auto;
    }
    .stAlert {
        background-color: #ebf8ff;
        color: #2b6cb0;
        border-radius: 8px;
        border: none;
        padding: 0.75rem 1rem;
    }
    .stSpinner {
        color: #4299e1;
    }
</style>
""", unsafe_allow_html=True)

st.markdown('<div class="main-container">', unsafe_allow_html=True)
st.title("💻 Sleek AI Code Assistant")
st.markdown('<p class="subtitle">Powered by Google Gemini</p>', 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..."):
            completed_text = generate_response(prompt)
            if "Error" in completed_text:
                st.error(completed_text)
            else:
                optimized_code = optimize_code(completed_text)
                st.success("Code generated and optimized successfully!")
                
                st.markdown('<div class="output-container">', unsafe_allow_html=True)
                st.markdown('<div class="code-block">', unsafe_allow_html=True)
                st.code(optimized_code)
                st.markdown('</div>', unsafe_allow_html=True)
                st.markdown('</div>', unsafe_allow_html=True)

st.markdown("""
<div style='text-align: center; margin-top: 2rem; color: #4a5568;'>
    Created with ❤️ by Your Sleek AI Code Assistant
</div>
""", unsafe_allow_html=True)

st.markdown('</div>', unsafe_allow_html=True)