app.py

import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import openai

# Constants
HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"

# Initialize session state
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = []
if 'terminal_history' not in st.session_state:
    st.session_state.terminal_history = []
if 'workspace_projects' not in st.session_state:
    st.session_state.workspace_projects = {}
if 'available_agents' not in st.session_state:
    st.session_state.available_agents = []
if 'current_state' not in st.session_state:
    st.session_state.current_state = {
        'toolbox': {},
        'workspace_chat': {}
    }

# AI Agent class
class AIAgent:
    def __init__(self, name, description, skills):
        self.name = name
        self.description = description
        self.skills = skills

    def create_agent_prompt(self):
        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
        agent_prompt = f"""
        As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
        {skills_str}
        I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
        """
        return agent_prompt

    def autonomous_build(self, chat_history, workspace_projects):
        summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
        summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
        next_step = "Based on the current state, the next logical step is to implement the main application logic."
        return summary, next_step

# Functions for agent management
def save_agent_to_file(agent):
    if not os.path.exists(AGENT_DIRECTORY):
        os.makedirs(AGENT_DIRECTORY)
    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
    config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
    with open(file_path, "w") as file:
        file.write(agent.create_agent_prompt())
    with open(config_path, "w") as file:
        file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
    st.session_state.available_agents.append(agent.name)
    commit_and_push_changes(f"Add agent {agent.name}")

def load_agent_prompt(agent_name):
    file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
    if os.path.exists(file_path):
        with open(file_path, "r") as file:
            agent_prompt = file.read()
        return agent_prompt
    else:
        return None

def create_agent_from_text(name, text):
    skills = text.split('\n')
    agent = AIAgent(name, "AI agent created from text input.", skills)
    save_agent_to_file(agent)
    return agent.create_agent_prompt()

# OpenAI GPT-3 API setup for text generation
openai.api_key = st.secrets["OPENAI_API_KEY"]

# Initialize the Hugging Face model and tokenizer
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)

# Tool Box UI elements
def toolbox():
    st.header("Tool Box")

    # List available agents
    for agent in st.session_state.available_agents:
        st.markdown(f"### {agent}")
        st.write(agent.description)
        if st.button(f'Chat with {agent}'):
            chat_with_agent(agent)

    # Add new agents
    if st.session_state['toolbox'].get('new_agent') is None:
        st.session_state['toolbox']['new_agent'] = {}

    st.text_input("Agent Name", key='name', on_change=update_agent)
    st.text_area("Agent Description", key='description', on_change=update_agent)
    st.text_input("Skills (comma-separated)", key='skills', on_change=update_agent)

    if st.button('Create New Agent'):
        skills = [s.strip() for s in st.session_state['toolbox']['new_agent'].get('skills', '').split(',')]
        new_agent = AIAgent(st.session_state['toolbox']['new_agent'].get('name'),
                            st.session_state['toolbox']['new_agent'].get('description'), skills)
        st.session_state.available_agents.append(new_agent)

def update_agent():
    st.session_state['toolbox']['new_agent'] = {
        'name': st.session_state.name,
        'description': st.session_state.description,
        'skills': st.session_state.skills
    }

def chat_with_agent(agent_name):
    st.subheader(f"Chat with {agent_name}")
    chat_input = st.text_area("Enter your message:")
    if st.button("Send"):
        chat_response = chat_interface_with_agent(chat_input, agent_name)
        st.session_state.chat_history.append((chat_input, chat_response))
        st.write(f"{agent_name}: {chat_response}")

# Workspace UI elements
def workspace():
    st.header("Workspace")

    # Project selection and interaction
    for project, details in st.session_state.workspace_projects.items():
        st.write(f"Project: {project}")
        for file in details['files']:
            st.write(f" - {file}")

    if st.button('Add New Project'):
        new_project = {'name': '', 'description': '', 'files': []}
        st.session_state.workspace_projects[new_project['name']] = new_project

# Main function to display the app
def main():
    toolbox()
    workspace()

if __name__ == "__main__":
    main()

# Additional functionalities
def commit_and_push_changes(commit_message):
    commands = [
        "git add .",
        f"git commit -m '{commit_message}'",
        "git push"
    ]
    for command in commands:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
        if result.returncode != 0:
            st.error(f"Error executing command '{command}': {result.stderr}")
            break

def chat_interface_with_agent(input_text, agent_name):
    agent_prompt = load_agent_prompt(agent_name)
    if agent_prompt is None:
        return f"Agent {agent_name} not found."

    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
    max_input_length = 900
    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
    if input_ids.shape[1] > max_input_length:
        input_ids = input_ids[:, :max_input_length]

    outputs = model.generate(input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def workspace_interface(project_name):
    project_path = os.path.join(PROJECT_ROOT, project_name)
    if not os.path.exists(PROJECT_ROOT):
        os.makedirs(PROJECT_ROOT)
    if not os.path.exists(project_path):
        os.makedirs(project_path)
        st.session_state.workspace_projects[project_name] = {"files": []}
        st.session_state.current_state['workspace_chat']['project_name'] = project_name
        commit_and_push_changes(f"Create project {project_name}")
        return f"Project {project_name} created successfully."
    else:
        return f"Project {project_name} already exists."

def add_code_to_workspace(project_name, code, file_name):
    project_path = os.path.join(PROJECT_ROOT, project_name)
    if os.path.exists(project_path):
        file_path = os.path.join(project_path, file_name)
        with open(file_path, "w") as file:
            file.write(code)
        st.session_state.workspace_projects[project_name]["files"].append(file_name)
        st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
        commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
        return f"Code added to {file_name} in project {project_name} successfully."
    else:
        return f"Project {project_name} does not exist."

def terminal_interface(command, project_name=None):
    if project_name:
        project_path = os.path.join(PROJECT_ROOT, project_name)
        if not os.path.exists(project_path):
            return f"Project {project_name} does not exist."
        result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
    else:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
    if result.returncode == 0:
        st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
        return result.stdout
    else:
        st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
        return result.stderr

def summarize_text(text):
    summarizer = pipeline("summarization")
    summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
    st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
    return summary[0]['summary_text']

def sentiment_analysis(text):
    analyzer = pipeline("sentiment-analysis")
    sentiment = analyzer(text)
    st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
    return sentiment[0]

def generate_code(code_idea):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are an expert software developer."},
            {"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
        ]
    )
    generated_code = response.choices[0].message['content'].strip()
    st.session_state.current_state['toolbox']['generated_code'] = generated_code
    return generated_code

def translate_code(code, input_language, output_language):
    language_extensions = {
        "Python": ".py",
        "JavaScript": ".js",
        # Add more languages and their extensions here
    }
    if input_language not in language_extensions:
        raise ValueError(f"Invalid input language: {input_language}")
    if output_language not in language_extensions:
        raise ValueError(f"Invalid output language: {output_language}")

    prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are an expert software developer."},
            {"role": "user", "content": prompt}
        ]
    )
    translated_code = response.choices[0].message['content'].strip()
    st.session_state.current_state['toolbox']['translated_code'] = translated_code
    return translated_code

# Streamlit App
st.title("AI Agent Creator")

# Sidebar navigation
st.sidebar.title("Navigation")
app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])

if app_mode == "AI Agent Creator":
    # AI Agent Creator
    st.header("Create an AI Agent from Text")

    st.subheader("From Text")
    agent_name = st.text_input("Enter agent name:")
    text_input = st.text_area("Enter skills (one per line):")
    if st.button("Create Agent"):
        agent_prompt = create_agent_from_text(agent_name, text_input)
        st.success(f"Agent '{agent_name}' created and saved successfully.")
        st.session_state.available_agents.append(agent_name)

elif app_mode == "Tool Box":
    # Tool Box
    st.header("AI-Powered Tools")

    # Chat Interface
    st.subheader("Chat with CodeCraft")
    chat_input = st.text_area("Enter your message:")
    if st.button("Send"):
        if chat_input.startswith("@"):
            agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
            chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
            chat_response = chat_interface_with_agent(chat_input, agent_name)
            st.session_state.chat_history.append((chat_input, chat_response))
            st.write(f"{agent_name}: {chat_response}")

    # Code Generation
    st.subheader("Generate Code")
    code_idea = st.text_area("Enter your code idea:")
    if st.button("Generate Code"):
        generated_code = generate_code(code_idea)
        st.code(generated_code, language='python')

    # Code Translation
    st.subheader("Translate Code")
    code = st.text_area("Enter your code:")
    input_language = st.selectbox("Input Language", ["Python", "JavaScript"])
    output_language = st.selectbox("Output Language", ["Python", "JavaScript"])
    if st.button("Translate Code"):
        translated_code = translate_code(code, input_language, output_language)
        st.code(translated_code, language=output_language.lower())

    # Summarization
    st.subheader("Summarize Text")
    text_to_summarize = st.text_area("Enter text to summarize:")
    if st.button("Summarize"):
        summary = summarize_text(text_to_summarize)
        st.write(summary)

    # Sentiment Analysis
    st.subheader("Sentiment Analysis")
    text_to_analyze = st.text_area("Enter text for sentiment analysis:")
    if st.button("Analyze Sentiment"):
        sentiment = sentiment_analysis(text_to_analyze)
        st.write(sentiment)

elif app_mode == "Workspace Chat App":
    # Workspace Chat App
    st.header("Workspace Chat App")

    # Project Management
    st.subheader("Manage Projects")
    project_name = st.text_input("Enter project name:")
    if st.button("Create Project"):
        project_message = workspace_interface(project_name)
        st.success(project_message)

    # Add Code to Project
    st.subheader("Add Code to Project")
    project_name_for_code = st.text_input("Enter project name for code:")
    code_content = st.text_area("Enter code content:")
    file_name = st.text_input("Enter file name:")
    if st.button("Add Code"):
        add_code_message = add_code_to_workspace(project_name_for_code, code_content, file_name)
        st.success(add_code_message)

    # Terminal Interface
    st.subheader("Terminal Interface")
    terminal_command = st.text_area("Enter terminal command:")
    project_name_for_terminal = st.text_input("Enter project name for terminal (optional):")
    if st.button("Run Command"):
        terminal_output = terminal_interface(terminal_command, project_name_for_terminal)
        st.text(terminal_output)