import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
import re
from typing import Dict, Any
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from concurrent.futures import ThreadPoolExecutor
import openai

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

# Global state to manage communication between Tool Box and Workspace Chat App
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': {}
    }
if 'current_agent' not in st.session_state:
    st.session_state.current_agent = None

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):
        """Autonomous build logic that continues based on the state of chat history and 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()])

        # Use an LLM to generate a summary of the current state and suggest the next step
        model_name = "gpt-3.5-turbo"  # You can choose a different LLM if you prefer
        response = openai.ChatCompletion.create(
            model=model_name,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant that can help developers with building projects. "},
                {"role": "user", "content": f"Based on the following summary, what is the next logical step in the development process? \n\n{summary}"}
            ]
        )
        next_step = response.choices[0].message['content']
        return summary, next_step

def save_agent_to_file(agent):
    """Saves the agent's prompt to a file locally and then commits to the Hugging Face repository."""
    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):
    """Loads an agent prompt from a file."""
    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()

# Chat interface using a selected agent
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."

    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
    model_name = "gpt2"
    try:
        model = AutoModelForCausalLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
        response = generator(
            f"{agent_prompt}\n\nUser: {input_text}\nAgent:",
            max_length=1024,
            num_return_sequences=1,
            do_sample=True,
            top_k=50,
            temperature=0.7,
        )[0]["generated_text"]
        return response
    except Exception as e:
        return f"Error: {e}"

# Chat interface for CodeCraft
def chat_interface(input_text):
    """Handles interactions with the CodeCraft AI agent."""
    # Define a pre-defined prompt for CodeCraft
    codecraft_prompt = """
    You are CodeCraft, a helpful and knowledgeable AI assistant specializing in software development. 
    You are designed to provide guidance, code snippets, and solutions to developers. 
    Please answer user questions in a comprehensive and informative manner.
    """

    # Load the GPT-2 model
    model_name = "gpt2"  # You can use a more advanced model like GPT-3 or GPT-4 if you have access
    model = AutoModelForCausalLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

    # Combine the agent prompt with user input
    combined_input = f"{codecraft_prompt}\n\nUser: {input_text}\nCodeCraft:"

    # Generate chatbot response
    response = generator(
        combined_input,
        max_length=1024,
        num_return_sequences=1,
        do_sample=True,
        top_k=50,
        temperature=0.7,
    )[0]["generated_text"]
    return response

def commit_and_push_changes(commit_message):
    """Commits and pushes changes to the Hugging Face repository."""
    try:
        os.system(f"git add .")
        os.system(f"git commit -m '{commit_message}'")
        os.system(f"git push origin main")
        st.write(f"Changes committed and pushed to {HUGGING_FACE_REPO_URL}")
    except Exception as e:
        st.write(f"Error committing and pushing changes: {e}")

def format_code(code):
    """Formats the code using Black and checks for linting errors."""
    try:
        formatted_code = black.format_str(code, mode=black.FileMode())
        return formatted_code
    except Exception as e:
        return f"Error formatting code: {e}"

def lint_code(code):
    """Checks the code for linting errors using Pylint."""
    try:
        output = StringIO()
        lint.run(code, do_exit=False, output=output)
        return output.getvalue()
    except Exception as e:
        return f"Error linting code: {e}"

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 translate_code(code, input_language, output_language):
    language_extensions = {
        "Python": ".py",
        "JavaScript": ".js",
        "Java": ".java",
        "C++": ".cpp",
        "C#": ".cs",
        "Go": ".go",
        "Ruby": ".rb",
        "PHP": ".php",
        "Swift": ".swift",
        "Kotlin": ".kt",
        "TypeScript": ".ts",
        "Rust": ".rs",
        "Scala": ".scala",
        "Dart": ".dart",
        "Lua": ".lua",
        "HTML": ".html",
        "CSS": ".css",
        "SQL": ".sql"
    }

    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}")

    input_extension = language_extensions[input_language]
    output_extension = language_extensions[output_language]

    # Translate the code using the OpenAI API
    prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are an expert software developer."},
            {"role": "user", "content": prompt}
        ]
    )
    translated_code = response.choices[0].message['content'].strip()

    # Return the translated code
    st.session_state.current_state['toolbox']['translated_code'] = translated_code
    return translated_code

def generate_code(code_idea):
    # Replace this with a call to a Hugging Face model or your own logic
    # For example, using a text-generation pipeline:
    generator = pipeline('text-generation', model='gpt4o')
    generated_code = generator(code_idea, max_length=10000, num_return_sequences=1)[0]['generated_text']
    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}"}
    ]
    st.session_state.current_state['toolbox']['generated_code'] = generated_code
    return generated_code

def run_git_command(command):
    try:
        result = subprocess.run(command, capture_output=True, text=True, shell=True, check=True)
        return True, result.stdout
    except subprocess.CalledProcessError as e:
        return False, f"Error: {e.stderr}"
    except Exception as e:
        return False, f"Unexpected error: {str(e)}"

def is_git_installed():
    try:
        subprocess.run(["git", "--version"], capture_output=True, check=True)
        return True
    except:
        return False

def is_git_repository():
    return os.path.isdir('.git')

st.title("Git Configuration Helper")

if not is_git_installed():
    st.error("Git is not installed or not in the system PATH. Please install Git and try again.")
    st.stop()

st.write("This app helps you set up your Git identity.")

email = st.text_input("Enter your email address:", "you@example.com")
name = st.text_input("Enter your name:", "Your Name")

scope = st.radio("Select configuration scope:", ("Global", "Local"))

if scope == "Local" and not is_git_repository():
    st.warning("You're not in a Git repository. Local configuration will not have any effect.")

if st.button("Set Git Configuration"):
    global_flag = "--global" if scope == "Global" else ""

    email_command = f"git config {global_flag} user.email \"{email}\""
    name_command = f"git config {global_flag} user.name \"{name}\""

    success, email_result = run_git_command(email_command)
    if success:
        st.success("Email configuration set successfully!")
    else:
        st.error(f"Failed to set email configuration: {email_result}")

    success, name_result = run_git_command(name_command)
    if success:
        st.success("Name configuration set successfully!")
    else:
        st.error(f"Failed to set name configuration: {name_result}")
    st.write("---")

if st.button("View Current Git Configuration"):
    success, config_result = run_git_command("git config --list")
    if success:
        st.write("Current Git Configuration:")
        st.code(config_result)
    else:
        st.error(f"Failed to retrieve Git configuration: {config_result}")

# 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)
        else:
            chat_response = chat_interface(chat_input)
        st.session_state.chat_history.append((chat_input, chat_response))
        st.write(f"CodeCraft: {chat_response}")

    # Terminal Interface
    st.subheader("Terminal")
    terminal_input = st.text_input("Enter a command:")
    if st.button("Run"):
        terminal_output = terminal_interface(terminal_input)
        st.session_state.terminal_history.append((terminal_input, terminal_output))
        st.code(terminal_output, language="bash")

    # Code Editor Interface
    st.subheader("Code Editor")
    code_editor = st.text_area("Write your code:", height=300)
    if st.button("Format & Lint"):
        formatted_code = format_code(code_editor)
        lint_message = lint_code(code_editor)
        st.code(formatted_code, language="python")
        st.info(lint_message)

    # Text Summarization Tool
    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(f"Summary: {summary}")

    # Sentiment Analysis Tool
    st.subheader("Sentiment Analysis")
    sentiment_text = st.text_area("Enter text for sentiment analysis:")
    if st.button("Analyze Sentiment"):
        sentiment = sentiment_analysis(sentiment_text)
        st.write(f"Sentiment: {sentiment}")

    # Text Translation Tool (Code Translation)
    st.subheader("Translate Code")
    code_to_translate = st.text_area("Enter code to translate:")
    source_language = st.text_input("Enter source language (e.g. 'Python'):")
    target_language = st.text_input("Enter target language (e.g. 'JavaScript'):")
    if st.button("Translate Code"):
        translated_code = translate_code(code_to_translate, source_language, target_language)
        st.code(translated_code, language=target_language.lower())

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

    # Display Preset Commands
    st.subheader("Preset Commands")
    preset_commands = {
        "Create a new project": "create_project('project_name')",
        "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
        "Run terminal command": "terminal_interface('command', 'project_name')",
        "Generate code": "generate_code('code_idea')",
        "Summarize text": "summarize_text('text')",
        "Analyze sentiment": "sentiment_analysis('text')",
        "Translate code": "translate_code('code', 'source_language', 'target_language')",
    }
    for command_name, command in preset_commands.items():
        st.write(f"{command_name}: {command}")

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

    # Project Workspace Creation
    st.subheader("Create a New Project")
    project_name = st.text_input("Enter project name:")
    if st.button("Create Project"):
        workspace_status = workspace_interface(project_name)
        st.success(workspace_status)

    # Add Code to Workspace
    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 with Project Context
    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 for Guidance
    st.subheader("Chat with CodeCraft 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"CodeCraft: {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"CodeCraft: {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", st.session_state.available_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.write(st.session_state.current_state)

def show_chat_history(current_agent):
    """Displays the chat history."""
    if current_agent in st.session_state.current_state['workspace_chat']:
        chat_msgs = st.session_state.current_state['workspace_chat'][current_agent]['messages']
        for msg_input, _ in reversed(chat_msgs):
            st.markdown(f"<s>{current_agent}: {msg_input}</s>", unsafe_allow_html=True)
    return ""

def get_agent_response(agent, input_text):
    """Gets the response from an agent for a given input."""
    if agent is None:
        return "No agent selected."
    elif agent == "CodeCraft":
        return chat_interface(input_text)
    else:
        return chat_interface_with_agent(input_text, agent)

def send_message_to_agent(agent, input_text):
    """Sends a message to an agent and displays its response."""
    output_text = get_agent_response(agent, input_text)
    st.markdown(f"{agent}: {output_text}")

def process_chat_input(input_text, current_agent):
    """Processes a chat input by checking special characters and forwarding it to appropriate handlers."""
    if re.match(r'^\W+$', input_text): # Mention other agents
        words = input_text[1:].split(' ')
        mentioned_agents = [' '.join(words[:i]+['']) for i in range(len(words)+1)]
        if len(mentioned_agents) > 1:
            del mentioned_agents[-1]
        mention_msg = f"@{current_agent}, you mentioned: {' | '.join(mentioned_agents)}"
        st.markdown(mention_msg)
        for agent in mentioned_agents:
            if agent != '' and agent in st.session_state.available_agents:
                st.session_state.current_state['workspace_chat'][agent] = {'messages': [], 'active': True}
                st.session_state.current_state['workspace_chat'][current_agent]['messages'].append((input_text, ""))
    elif re.fullmatch(r'\d+', input_text): # Select agent by number
        if int(input_text)-1 < len(st.session_state.available_agents):
            st.session_state.current_agent = st.session_state.available_agents[int(input_text)-1]
    elif input_text == '/clear': # Clear conversation
        st.session_state.current_state['workspace_chat'][current_agent]['messages'] = []
    elif input_text == '/exit': # Exit conversation
        st.session_state.current_agent = None
    else:
        st.session_state.current_state['workspace_chat'][current_agent]['messages'].append((input_text, ""))

if __name__ == "__main__":
    main()