app.py

# config.py
import os
from huggingface_hub import InferenceClient
from transformers import pipeline

# Initialize clients and models
MIXTRAL_CLIENT = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
LLAMA_PIPELINE = pipeline("text-generation", model="bartowski/Llama-3-8B-Instruct-Coder-GGUF")

AGENTS = [
    "WEB_DEV",
    "AI_SYSTEM_PROMPT",
    "PYTHON_CODE_DEV",
    "CODE_REVIEW_ASSISTANT",
    "CONTENT_WRITER_EDITOR",
    "QUESTION_GENERATOR",
    "HUGGINGFACE_FILE_DEV",
]

# ai_agent.py
import random
from typing import List, Dict, Tuple

class AIAgent:
    def __init__(self, name: str, description: str, skills: List[str]):
        self.name = name
        self.description = description
        self.skills = skills

    def create_agent_prompt(self) -> str:
        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
        return 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.
        """

    def autonomous_build(self, chat_history: List[Tuple[str, str]], workspace_projects: Dict[str, Dict]) -> Tuple[str, str]:
        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

# utils.py
import os
import subprocess
from typing import List, Tuple

def format_prompt(message: str, history: List[Tuple[str, str]]) -> str:
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST] {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt: str, history: List[Tuple[str, str]], agent_name: str = AGENTS[0], sys_prompt: str = "", 
             temperature: float = 0.9, max_new_tokens: int = 256, top_p: float = 0.95, repetition_penalty: float = 1.0):
    seed = random.randint(1, 1111111111111111)
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=seed,
    )
    formatted_prompt = format_prompt(f"{sys_prompt}, {prompt}", history)
    stream = MIXTRAL_CLIENT.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        output += response.token.text
        yield output
    return output

def terminal_interface(command: str, project_name: str) -> str:
    try:
        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_name)
        return result.stdout if result.returncode == 0 else result.stderr
    except Exception as e:
        return str(e)

def add_code_to_workspace(project_name: str, code: str, file_name: str) -> str:
    project_path = os.path.join(os.getcwd(), project_name)
    os.makedirs(project_path, exist_ok=True)
    file_path = os.path.join(project_path, file_name)
    with open(file_path, 'w') as file:
        file.write(code)
    return f"Added {file_name} to {project_name}"

# main.py
import streamlit as st
import gradio as gr
from config import AGENTS
from ai_agent import AIAgent
from utils import generate, terminal_interface, add_code_to_workspace

# Streamlit UI
def main():
    st.title("DevToolKit: AI-Powered Development Environment")

    # Project Management
    st.header("Project Management")
    project_name = st.text_input("Enter project name:")
    if st.button("Create Project"):
        if project_name not in st.session_state.workspace_projects:
            st.session_state.workspace_projects[project_name] = {'files': []}
            st.success(f"Created project: {project_name}")
        else:
            st.warning(f"Project {project_name} already exists")

    # Code Addition
    st.subheader("Add Code to Workspace")
    code_to_add = st.text_area("Enter code to add to workspace:")
    file_name = st.text_input("Enter file name (e.g. 'app.py'):")
    if st.button("Add Code"):
        add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
        st.success(add_code_status)

    # Terminal Interface
    st.subheader("Terminal (Workspace Context)")
    terminal_input = st.text_input("Enter a command within the workspace:")
    if st.button("Run Command"):
        terminal_output = terminal_interface(terminal_input, project_name)
        st.code(terminal_output, language="bash")

    # Chat Interface
    st.subheader("Chat with DevToolKit for Guidance")
    chat_input = st.text_area("Enter your message for guidance:")
    if st.button("Get Guidance"):
        chat_response = next(generate(chat_input, st.session_state.chat_history))
        st.session_state.chat_history.append((chat_input, chat_response))
        st.write(f"DevToolKit: {chat_response}")

    # Display Chat History
    st.subheader("Chat History")
    for user_input, response in st.session_state.chat_history:
        st.write(f"User: {user_input}")
        st.write(f"DevToolKit: {response}")

    # Display Terminal History
    st.subheader("Terminal History")
    for command, output in st.session_state.terminal_history:
        st.write(f"Command: {command}")
        st.code(output, language="bash")

    # Display Projects and Files
    st.subheader("Workspace Projects")
    for project, details in st.session_state.workspace_projects.items():
        st.write(f"Project: {project}")
        for file in details['files']:
            st.write(f" - {file}")

    # Chat with AI Agents
    st.subheader("Chat with AI Agents")
    selected_agent = st.selectbox("Select an AI agent", AGENTS)
    agent_chat_input = st.text_area("Enter your message for the agent:")
    if st.button("Send to Agent"):
        agent_chat_response = next(generate(agent_chat_input, st.session_state.chat_history, agent_name=selected_agent))
        st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
        st.write(f"{selected_agent}: {agent_chat_response}")

    # Automate Build Process
    st.subheader("Automate Build Process")
    if st.button("Automate"):
        agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
        summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
        st.write("Autonomous Build Summary:")
        st.write(summary)
        st.write("Next Step:")
        st.write(next_step)

    # Display current state for debugging
    st.sidebar.subheader("Current State")
    st.sidebar.json(st.session_state.current_state)

if __name__ == "__main__":
    main()

# gradio_interface.py
import gradio as gr
from config import AGENTS
from utils import generate

def create_gradio_interface():
    additional_inputs = [
        gr.Dropdown(label="Agents", choices=[s for s in AGENTS], value=AGENTS[0], interactive=True),
        gr.Textbox(label="System Prompt", max_lines=1, interactive=True),
        gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"),
        gr.Slider(label="Max new tokens", value=1048*10, minimum=0, maximum=1000*10, step=64, interactive=True, info="The maximum numbers of new tokens"),
        gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
        gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens"),
    ]

    examples = [
        ["Create a simple web application using Flask", AGENTS[0], None, None, None, None],
        ["Generate a Python script to perform a linear regression analysis", AGENTS[2], None, None, None, None],
        ["Create a Dockerfile for a Node.js application", AGENTS[1], None, None, None, None],
    ]

    return gr.ChatInterface(
        fn=generate,
        chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
        additional_inputs=additional_inputs,
        title="DevToolKit AI Assistant",
        examples=examples,
        concurrency_limit=20,
    )

if __name__ == "__main__":
    interface = create_gradio_interface()
    interface.launch(show_api=True)