app.py

import os
import sys
import subprocess
import streamlit as st
from huggingface_hub import InferenceClient
import gradio as gr
import random
import prompts
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO

# Initialize the InferenceClient for Mixtral-8x7B-Instruct-v0.1
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

# Initialize the pipeline for Llama-3-8B-Instruct-Coder-GGUF
pipe = pipeline("text-generation", model="bartowski/Llama-3-8B-Instruct-Coder-GGUF")

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

# Global state management
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': {}
    }

# Define the agents
agents = [
    "WEB_DEV",
    "AI_SYSTEM_PROMPT",
    "PYTHON_CODE_DEV",
    "CODE_REVIEW_ASSISTANT",
    "CONTENT_WRITER_EDITOR",
    "QUESTION_GENERATOR",
    "HUGGINGFACE_FILE_DEV",
]

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

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

def generate(prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    seed = random.randint(1, 1111111111111111)
    agent = getattr(prompts, agent_name, prompts.WEB_DEV_SYSTEM_PROMPT)
    system_prompt = agent

    generate_kwargs = dict(
        temperature=float(temperature),
        max_new_tokens=max_new_tokens,
        top_p=float(top_p),
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=seed,
    )

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    stream = 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 chat_interface(chat_input):
    response = generate(chat_input, st.session_state.chat_history)
    return response

def chat_interface_with_agent(chat_input, agent_name):
    agent_prompt = getattr(prompts, agent_name, prompts.WEB_DEV_SYSTEM_PROMPT)
    response = generate(chat_input, st.session_state.chat_history, agent_name=agent_name, sys_prompt=agent_prompt)
    return response

def terminal_interface(command, project_name):
    # Implement terminal functionality here
    return f"Executed command: {command} in project: {project_name}"

def add_code_to_workspace(project_name, code, file_name):
    if project_name not in st.session_state.workspace_projects:
        st.session_state.workspace_projects[project_name] = {'files': []}
    st.session_state.workspace_projects[project_name]['files'].append(file_name)
    return f"Added {file_name} to {project_name}"

# Streamlit UI
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 = chat_interface(chat_input)
    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 = chat_interface_with_agent(agent_chat_input, 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)

# 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, ],
    # Add more examples as needed
]

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,
).launch(show_api=True)