app.txt

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, GradientBoostingClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import git
import spacy
from spacy.lang.en import English
import boto3
import unittest
import docker
import sympy as sp
from scipy.optimize import minimize, differential_evolution
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display
from tenacity import retry, stop_after_attempt, wait_fixed
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import AutoTokenizer, AutoModel
import networkx as nx
from sklearn.cluster import KMeans
from scipy.stats import ttest_ind
from statsmodels.tsa.arima.model import ARIMA
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import cv2
from PIL import Image
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions

# 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": 0.4,
    "top_p": 0.8,
    "top_k": 50,
    "max_output_tokens": 4096,
}

model = genai.GenerativeModel(
    model_name="gemini-1.5-pro",
    generation_config=generation_config,
    system_instruction="""
    You are Ath, an ultra-advanced AI code assistant with expertise across multiple domains including machine learning, data science, web development, cloud computing, and more. Your responses should showcase cutting-edge techniques, best practices, and innovative solutions.
    """
)
chat_session = model.start_chat(history=[])

@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
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):
    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):
    # Implement GitHub API interaction here
    pass

def interact_with_api(api_url):
    response = requests.get(api_url)
    return response.json()

def train_advanced_ml_model(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    models = {
        'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
        'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
    }
    results = {}
    for name, model in models.items():
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        results[name] = {
            'accuracy': accuracy_score(y_test, y_pred),
            'precision': precision_score(y_test, y_pred, average='weighted'),
            'recall': recall_score(y_test, y_pred, average='weighted'),
            'f1': f1_score(y_test, y_pred, average='weighted')
        }
    return results

def handle_error(error):
    st.error(f"An error occurred: {error}")
    # Implement advanced error logging and notification system here

def initialize_git_repo(repo_path):
    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):
    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):
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(user_input)
    return doc

def interact_with_cloud_services(service_name, action, params):
    client = boto3.client(service_name)
    response = getattr(client, action)(**params)
    return response

def run_tests():
    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 execute_code_in_docker(code):
    client = docker.from_env()
    try:
        container = client.containers.run(
            image="python:3.9",
            command=f"python -c '{code}'",
            detach=True,
            remove=True
        )
        result = container.wait()
        logs = container.logs().decode('utf-8')
        return logs, result['StatusCode']
    except Exception as e:
        return f"Error: {e}", 1

def solve_complex_equation(equation):
    x, y, z = sp.symbols('x y z')
    eq = sp.Eq(eval(equation))
    solution = sp.solve(eq)
    return solution

def advanced_optimization(function, bounds):
    result = differential_evolution(lambda x: eval(function), bounds)
    return result.x, result.fun

def visualize_complex_data(data):
    df = pd.DataFrame(data)
    fig, axs = plt.subplots(2, 2, figsize=(16, 12))
    
    sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=axs[0, 0])
    axs[0, 0].set_title('Correlation Heatmap')
    
    sns.pairplot(df, diag_kind='kde', ax=axs[0, 1])
    axs[0, 1].set_title('Pairplot')
    
    df.plot(kind='box', ax=axs[1, 0])
    axs[1, 0].set_title('Box Plot')
    
    sns.violinplot(data=df, ax=axs[1, 1])
    axs[1, 1].set_title('Violin Plot')
    
    plt.tight_layout()
    return fig

def analyze_complex_data(data):
    df = pd.DataFrame(data)
    summary = df.describe()
    correlation = df.corr()
    skewness = df.skew()
    kurtosis = df.kurtosis()
    return {
        'summary': summary,
        'correlation': correlation,
        'skewness': skewness,
        'kurtosis': kurtosis
    }

def train_deep_learning_model(X, y):
    class DeepNN(nn.Module):
        def __init__(self, input_size):
            super(DeepNN, self).__init__()
            self.fc1 = nn.Linear(input_size, 64)
            self.fc2 = nn.Linear(64, 32)
            self.fc3 = nn.Linear(32, 1)
        
        def forward(self, x):
            x = torch.relu(self.fc1(x))
            x = torch.relu(self.fc2(x))
            x = torch.sigmoid(self.fc3(x))
            return x

    X_tensor = torch.FloatTensor(X.values)
    y_tensor = torch.FloatTensor(y.values)

    model = DeepNN(X.shape[1])
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters())

    epochs = 100
    for epoch in range(epochs):
        optimizer.zero_grad()
        outputs = model(X_tensor)
        loss = criterion(outputs, y_tensor.unsqueeze(1))
        loss.backward()
        optimizer.step()

    return model

def perform_nlp_analysis(text):
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)
    
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    tokens = [token.text for token in doc]
    pos_tags = [(token.text, token.pos_) for token in doc]
    
    sia = SentimentIntensityAnalyzer()
    sentiment = sia.polarity_scores(text)
    
    return {
        'entities': entities,
        'tokens': tokens,
        'pos_tags': pos_tags,
        'sentiment': sentiment
    }

def perform_image_analysis(image_path):
    img = cv2.imread(image_path)
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # Perform object detection
    model = ResNet50(weights='imagenet')
    img_resized = cv2.resize(img_rgb, (224, 224))
    img_array = image.img_to_array(img_resized)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = preprocess_input(img_array)
    
    predictions = model.predict(img_array)
    decoded_predictions = decode_predictions(predictions, top=3)[0]
    
    # Perform edge detection
    edges = cv2.Canny(img, 100, 200)
    
    return {
        'predictions': decoded_predictions,
        'edges': edges
    }

def perform_time_series_analysis(data):
    df = pd.DataFrame(data)
    model = ARIMA(df, order=(1, 1, 1))
    results = model.fit()
    forecast = results.forecast(steps=5)
    return {
        'model_summary': results.summary(),
        'forecast': forecast
    }

def perform_graph_analysis(nodes, edges):
    G = nx.Graph()
    G.add_nodes_from(nodes)
    G.add_edges_from(edges)
    
    centrality = nx.degree_centrality(G)
    clustering = nx.clustering(G)
    shortest_paths = dict(nx.all_pairs_shortest_path_length(G))
    
    return {
        'centrality': centrality,
        'clustering': clustering,
        'shortest_paths': shortest_paths
    }

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

# ... (Keep the existing CSS styles)

st.markdown('<div class="main-container">', unsafe_allow_html=True)
st.title("🚀 Ultra AI Code Assistant")
st.markdown('<p class="subtitle">Powered by Advanced AI and Domain Expertise</p>', unsafe_allow_html=True)

task_type = st.selectbox("Select Task Type", [
    "Code Generation",
    "Machine Learning",
    "Data Analysis",
    "Natural Language Processing",
    "Image Analysis",
    "Time Series Analysis",
    "Graph Analysis"
])

prompt = st.text_area("Enter your task description or code:", height=120)

if st.button("Execute Task"):
    if prompt.strip() == "":
        st.error("Please enter a valid prompt.")
    else:
        with st.spinner("Processing your request..."):
            try:
                if task_type == "Code Generation":
                    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('<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)
                        
                        repo_path = "./repo"
                        integrate_with_git(repo_path, optimized_code)
                        
                        test_result = run_tests()
                        if test_result.wasSuccessful():
                            st.success("All tests passed successfully!")
                        else:
                            st.error("Some tests failed. Please check the code.")
                        
                        execution_result, status_code = execute_code_in_docker(optimized_code)
                        if status_code == 0:
                            st.success("Code executed successfully in Docker!")
                            st.text(execution_result)
                        else:
                            st.error(f"Code execution failed: {execution_result}")
                
                elif task_type == "Machine Learning":
                    # For demonstration, we'll use a sample dataset
                    from sklearn.datasets import make_classification
                    X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
                    results = train_advanced_ml_model(X, y)
                    st.write("Machine Learning Model Performance:")
                    st.json(results)
                    
                    st.write("Deep Learning Model:")
                    deep_model = train_deep_learning_model(pd.DataFrame(X), pd.Series(y))
                    st.write(deep_model)
                
                elif task_type == "Data Analysis":
                    # For demonstration, we'll use a sample dataset
                    data = pd.DataFrame(np.random.randn(100, 5), columns=['A', 'B', 'C', 'D', 'E'])
                    analysis_results = analyze_complex_data(data)
                    st.write("Data Analysis Results:")
                    st.write(analysis_results['summary'])
                    st.write("Correlation Matrix:")
                    st.write(analysis_results['correlation'])
                    
                    fig = visualize_complex_data(data)
                    st.pyplot(fig)
                
                elif task_type == "Natural Language Processing":
                    nlp_results = perform_nlp_analysis(prompt)
                    st.write("NLP Analysis Results:")
                    st.json(nlp_results)
                    elif task_type == "Image Analysis":
                    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
                    if uploaded_file is not None:
                        image = Image.open(uploaded_file)
                        st.image(image, caption='Uploaded Image', use_column_width=True)
                        
                        # Save the uploaded image temporarily
                        with open("temp_image.jpg", "wb") as f:
                            f.write(uploaded_file.getbuffer())
                        
                        analysis_results = perform_image_analysis("temp_image.jpg")
                        
                        st.write("Image Analysis Results:")
                        st.write("Top 3 predictions:")
                        for i, (imagenet_id, label, score) in enumerate(analysis_results['predictions']):
                            st.write(f"{i + 1}: {label} ({score:.2f})")
                        
                        st.write("Edge Detection:")
                        st.image(analysis_results['edges'], caption='Edge Detection', use_column_width=True)
                        
                        # Remove the temporary image file
                        os.remove("temp_image.jpg")
                
                elif task_type == "Time Series Analysis":
                    # For demonstration, we'll use a sample time series dataset
                    dates = pd.date_range(start='1/1/2020', end='1/1/2021', freq='D')
                    values = np.random.randn(len(dates)).cumsum()
                    ts_data = pd.Series(values, index=dates)
                    
                    st.line_chart(ts_data)
                    
                    analysis_results = perform_time_series_analysis(ts_data)
                    st.write("Time Series Analysis Results:")
                    st.write(analysis_results['model_summary'])
                    st.write("Forecast for the next 5 periods:")
                    st.write(analysis_results['forecast'])
                
                elif task_type == "Graph Analysis":
                    # For demonstration, we'll use a sample graph
                    nodes = range(1, 11)
                    edges = [(1, 2), (1, 3), (2, 4), (2, 5), (3, 6), (3, 7), (4, 8), (5, 9), (6, 10)]
                    
                    analysis_results = perform_graph_analysis(nodes, edges)
                    st.write("Graph Analysis Results:")
                    st.write("Centrality:")
                    st.json(analysis_results['centrality'])
                    st.write("Clustering Coefficient:")
                    st.json(analysis_results['clustering'])
                    
                    # Visualize the graph
                    G = nx.Graph()
                    G.add_nodes_from(nodes)
                    G.add_edges_from(edges)
                    fig, ax = plt.subplots(figsize=(10, 8))
                    nx.draw(G, with_labels=True, node_color='lightblue', node_size=500, font_size=16, font_weight='bold', ax=ax)
                    st.pyplot(fig)
            
            except Exception as e:
                handle_error(e)

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

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

# Additional helper functions

def explain_code(code):
    """Generate an explanation for the given code using NLP techniques."""
    explanation = generate_response(f"Explain the following code:\n\n{code}")
    return explanation

def generate_unit_tests(code):
    """Generate unit tests for the given code."""
    unit_tests = generate_response(f"Generate unit tests for the following code:\n\n{code}")
    return unit_tests

def suggest_optimizations(code):
    """Suggest optimizations for the given code."""
    optimizations = generate_response(f"Suggest optimizations for the following code:\n\n{code}")
    return optimizations

def generate_documentation(code):
    """Generate documentation for the given code."""
    documentation = generate_response(f"Generate documentation for the following code:\n\n{code}")
    return documentation

# Add these new functions to the Streamlit UI
if task_type == "Code Generation":
    st.sidebar.header("Code Analysis Tools")
    if st.sidebar.button("Explain Code"):
        explanation = explain_code(optimized_code)
        st.sidebar.subheader("Code Explanation")
        st.sidebar.write(explanation)
    
    if st.sidebar.button("Generate Unit Tests"):
        unit_tests = generate_unit_tests(optimized_code)
        st.sidebar.subheader("Generated Unit Tests")
        st.sidebar.code(unit_tests)
    
    if st.sidebar.button("Suggest Optimizations"):
        optimizations = suggest_optimizations(optimized_code)
        st.sidebar.subheader("Suggested Optimizations")
        st.sidebar.write(optimizations)
    
    if st.sidebar.button("Generate Documentation"):
        documentation = generate_documentation(optimized_code)
        st.sidebar.subheader("Generated Documentation")
        st.sidebar.write(documentation)

# Add more advanced features
def perform_security_analysis(code):
    """Perform a basic security analysis on the given code."""
    security_analysis = generate_response(f"Perform a security analysis on the following code and suggest improvements:\n\n{code}")
    return security_analysis

def generate_api_documentation(code):
    """Generate API documentation for the given code."""
    api_docs = generate_response(f"Generate API documentation for the following code:\n\n{code}")
    return api_docs

def suggest_design_patterns(code):
    """Suggest appropriate design patterns for the given code."""
    design_patterns = generate_response(f"Suggest appropriate design patterns for the following code:\n\n{code}")
    return design_patterns

# Add these new functions to the Streamlit UI
if task_type == "Code Generation":
    st.sidebar.header("Advanced Code Analysis")
    if st.sidebar.button("Security Analysis"):
        security_analysis = perform_security_analysis(optimized_code)
        st.sidebar.subheader("Security Analysis")
        st.sidebar.write(security_analysis)
    
    if st.sidebar.button("Generate API Documentation"):
        api_docs = generate_api_documentation(optimized_code)
        st.sidebar.subheader("API Documentation")
        st.sidebar.write(api_docs)
    
    if st.sidebar.button("Suggest Design Patterns"):
        design_patterns = suggest_design_patterns(optimized_code)
        st.sidebar.subheader("Suggested Design Patterns")
        st.sidebar.write(design_patterns)

# Add a feature to generate a complete project structure
def generate_project_structure(project_description):
    """Generate a complete project structure based on the given description."""
    project_structure = generate_response(f"Generate a complete project structure for the following project description:\n\n{project_description}")
    return project_structure

# Add this new function to the Streamlit UI
if st.sidebar.button("Generate Project Structure"):
    project_description = st.sidebar.text_area("Enter project description:")
    if project_description:
        project_structure = generate_project_structure(project_description)
        st.sidebar.subheader("Generated Project Structure")
        st.sidebar.code(project_structure)

# Add a feature to suggest relevant libraries and frameworks
def suggest_libraries(code):
    """Suggest relevant libraries and frameworks for the given code."""
    suggestions = generate_response(f"Suggest relevant libraries and frameworks for the following code:\n\n{code}")
    return suggestions

# Add this new function to the Streamlit UI
if task_type == "Code Generation":
    if st.sidebar.button("Suggest Libraries"):
        library_suggestions = suggest_libraries(optimized_code)
        st.sidebar.subheader("Suggested Libraries and Frameworks")
        st.sidebar.write(library_suggestions)

# Add a feature to generate code in multiple programming languages
def translate_code(code, target_language):
    """Translate the given code to the specified target language."""
    translated_code = generate_response(f"Translate the following code to {target_language}:\n\n{code}")
    return translated_code

# Add this new function to the Streamlit UI
if task_type == "Code Generation":
    target_language = st.sidebar.selectbox("Select target language for translation", ["Python", "JavaScript", "Java", "C++", "Go"])
    if st.sidebar.button("Translate Code"):
        translated_code = translate_code(optimized_code, target_language)
        st.sidebar.subheader(f"Translated Code ({target_language})")
        st.sidebar.code(translated_code)

# Add a feature to generate a README file for the project
def generate_readme(project_description, code):
    """Generate a README file for the project based on the description and code."""
    readme_content = generate_response(f"Generate a README.md file for the following project:\n\nDescription: {project_description}\n\nCode:\n{code}")
    return readme_content

# Add this new function to the Streamlit UI
if task_type == "Code Generation":
    if st.sidebar.button("Generate README"):
        project_description = st.sidebar.text_area("Enter project description:")
        if project_description:
            readme_content = generate_readme(project_description, optimized_code)
            st.sidebar.subheader("Generated README.md")
            st.sidebar.markdown(readme_content)

# Add a feature to suggest code refactoring
def suggest_refactoring(code):
    """Suggest code refactoring improvements for the given code."""
    refactoring_suggestions = generate_response(f"Suggest code refactoring improvements for the following code:\n\n{code}")
    return refactoring_suggestions

# Add this new function to the Streamlit UI
if task_type == "Code Generation":
    if st.sidebar.button("Suggest Refactoring"):
        refactoring_suggestions = suggest_refactoring(optimized_code)
        st.sidebar.subheader("Refactoring Suggestions")
        st.sidebar.write(refactoring_suggestions)

# Add a feature to generate sample test data
def generate_test_data(code):
    """Generate sample test data for the given code."""
    test_data = generate_response(f"Generate sample test data for the following code:\n\n{code}")
    return test_data

# Add this new function to the Streamlit UI
if task_type == "Code Generation":
    if st.sidebar.button("Generate Test Data"):
        test_data = generate_test_data(optimized_code)
        st.sidebar.subheader("Generated Test Data")
        st.sidebar.code(test_data)

# Main execution
if __name__ == "__main__":
    st.sidebar.header("About")
    st.sidebar.info("This Ultra AI Code Assistant is powered by advanced AI models and incorporates expertise across multiple domains including software development, machine learning, data analysis, and more.")
    
    st.sidebar.header("Feedback")
    feedback = st.sidebar.text_area("Please provide any feedback or suggestions:")
    if st.sidebar.button("Submit Feedback"):
        # Here you would typically send this feedback to a database or email
        st.sidebar.success("Thank you for your feedback!")