import os
import sys
import subprocess
import base64
import json
from io import StringIO
from typing import Dict, List

import streamlit as st
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
from pylint import lint

# Add your Hugging Face API token here
hf_token = st.secrets["hf_token"]

# 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 = {}

# Load pre-trained RAG retriever
rag_retriever = pipeline("retrieval-question-answering", model="facebook/rag-token-base")

# Load pre-trained chat model
chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")

def process_input(user_input: str) -> str:
    # Input pipeline: Tokenize and preprocess user input
    input_ids = tokenizer(user_input, return_tensors="pt").input_ids
    attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask

    # RAG model: Generate response
    with torch.no_grad():
        output = rag_retriever(input_ids, attention_mask=attention_mask)
        response = output.generator_outputs[0].sequences[0]

    # Chat model: Refine response
    chat_input = tokenizer(response, return_tensors="pt")
    chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0)
    chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0)
    with torch.no_grad():
        chat_output = chat_model(**chat_input)
        refined_response = chat_output.sequences[0]

    # Output pipeline: Return final response
    return refined_response

class AIAgent:
    def __init__(self, name: str, description: str, skills: List[str], hf_api=None):
        self.name = name
        self.description = description
        self.skills = skills
        self._hf_api = hf_api
        self._hf_token = hf_token

    @property
    def hf_api(self):
        if not self._hf_api and self.has_valid_hf_token():
            self._hf_api = HfApi(token=self._hf_token)
        return self._hf_api

    def has_valid_hf_token(self):
        return bool(self._hf_token)

    async def autonomous_build(self, chat_history: List[str], workspace_projects: Dict[str, str], project_name: str, selected_model: str):
        # Continuation of previous methods
        summary = "Chat History:\n" + "\n".join(chat_history)
        summary += "\n\nWorkspace Projects:\n" + "\n".join(workspace_projects.items())

        # Analyze chat history and workspace projects to suggest actions
        # Example:
        # - Check if the user has requested to create a new file
        # - Check if the user has requested to install a package
        # - Check if the user has requested to run a command
        # - Check if the user has requested to generate code
        # - Check if the user has requested to translate code
        # - Check if the user has requested to summarize text
        # - Check if the user has requested to analyze sentiment

        # Generate a response based on the analysis
        next_step = "Based on the current state, the next logical step is to implement the main application logic."

        # Ensure project folder exists
        project_path = os.path.join(PROJECT_ROOT, project_name)
        if not os.path.exists(project_path):
            os.makedirs(project_path)

        # Create requirements.txt if it doesn't exist
        requirements_file = os.path.join(project_path, "requirements.txt")
        if not os.path.exists(requirements_file):
            with open(requirements_file, "w") as f:
                f.write("# Add your project's dependencies here\n")

        # Create app.py if it doesn't exist
        app_file = os.path.join(project_path, "app.py")
        if not os.path.exists(app_file):
            with open(app_file, "w") as f:
                f.write("# Your project's main application logic goes here\n")

        # Generate GUI code for app.py if requested
        if "create a gui" in summary.lower():
            gui_code = generate_code(
                "Create a simple GUI for this application", selected_model)
            with open(app_file, "a") as f:
                f.write(gui_code)

        # Run the default build process
        build_command = "pip install -r requirements.txt && python app.py"
        try:
            result = subprocess.run(
                build_command, shell=True, capture_output=True, text=True, cwd=project_path)
            st.write(f"Build Output:\n{result.stdout}")
            if result.stderr:
                st.error(f"Build Errors:\n{result.stderr}")
        except Exception as e:
            st.error(f"Build Error: {e}")

        return summary, next_step

def get_built_space_files() -> Dict[str, str]:
    # Replace with your logic to gather the files you want to deploy
    return {
        "app.py": "# Your Streamlit app code here",
        "requirements.txt": "streamlit\ntransformers"
        # Add other files as needed
    }

def save_agent_to_file(agent: AIAgent):
    """Saves the agent's prompt to a file."""
    if not os.path.exists(AGENT_DIRECTORY):
        os.makedirs(AGENT_DIRECTORY)
    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
    with open(file_path, "w") as file:
        file.write(agent.create_agent_prompt())
    st.session_state.available_agents.append(agent.name)

def load_agent_prompt(agent_name: str) -> str:
    """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: str, text: str) -> str:
    skills = text.split("\n")
    agent = AIAgent(name, "AI agent created from text input.", skills)
    save_agent_to_file(agent)
    return agent.create_agent_prompt()

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

    model_name = "MaziyarPanahi/Codestral-22B-v0.1-GGUF"import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO

HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
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': {}
    }

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()])

        next_step = "Based on the current state, the next logical step is to implement the main application logic."

        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)
    except EnvironmentError as e:
        return f"Error loading model: {e}"

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

    # Truncate input text to avoid exceeding the model's maximum length
    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]

    # Generate chatbot response
    outputs = model.generate(
        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, pad_token_id=tokenizer.eos_token_id # Set pad_token_id to 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]

# ... [rest of the translate_code function, but remove the OpenAI API call and replace it with your own logic] ...

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 translate_code(code, input_language, output_language):
    # Define a dictionary to map programming languages to their corresponding file extensions
    language_extensions = {
        "Python": "py",
        "JavaScript": "js",
        "Java": "java",
        "C++": "cpp",
        "C#": "cs",
        "Ruby": "rb",
        "Go": "go",
        "PHP": "php",
        "Swift": "swift",
        "TypeScript": "ts",
    }

    # Add code to handle edge cases such as invalid input and unsupported programming languages
    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}")

    # Use the dictionary to map the input and output languages to their corresponding file extensions
    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-4",
        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
    translated_code = response.choices[0].message['content'].strip()
    st.session_state.current_state['toolbox']['translated_code'] = translated_code
    return translated_code

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 commit_and_push_changes(commit_message):
    """Commits and pushes changes to the Hugging Face repository."""
    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 interact_with_web_interface(agent, api_key, url, payload):
    """
    Interacts with a web interface using the provided API key and payload.

    Args:
        agent: The AIAgent instance.
        api_key: The API key for the web interface.
        url: The URL of the web interface.
        payload: The payload to send to the web interface.

    Returns:
        The response from the web interface.
    """

    # Use the agent's knowledge to determine the appropriate HTTP method and headers.
    http_method = agent.get_http_method(url)
    headers = agent.get_headers(url)

    # Add the API key to the headers.
    headers["Authorization"] = f"Bearer {api_key}"

    # Send the request to the web interface.
    response = requests.request(http_method, url, headers=headers, json=payload)

    # Return the response.
    return response

def get_http_method(url):
    """
    Determines the appropriate HTTP method for the given URL.

    Args:
        url: The URL of the web interface.

    Returns:
        The HTTP method (e.g., "GET", "POST", "PUT", "DELETE").
    """

    # Use the agent's knowledge to determine the HTTP method.
    # For example, the agent might know that the URL is for a REST API endpoint that supports CRUD operations.

    return "GET"

def get_headers(url):
    """
    Determines the appropriate headers for the given URL.

    Args:
        url: The URL of the web interface.

    Returns:
        A dictionary of headers.
    """

    # Use the agent's knowledge to determine the headers.
    # For example, the agent might know that the web interface requires an "Authorization" header with an API key.

    return {"Content-Type": "application/json"}

# ... (rest of the code)

if app_mode == "Toolbox":

# 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, lint_message = code_editor_interface(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:")
    input_language = st.text_input("Enter input language (e.g. 'Python'):")
    output_language = st.text_input("Enter output language (e.g. 'JavaScript'):")
    if st.button("Translate Code"):
        translated_code = translate_code(code_to_translate, input_language, output_language)
        st.code(translated_code, language=output_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}")
        st.write("Files:")
        for file in details["files"]:
            st.write(f"- {file}")
    try:
        generator = pipeline("text-generation", model=model_name)
        generator.tokenizer.pad_token = generator.tokenizer.eos_token
        generated_response = generator(
            f"{agent_prompt}\n\nUser: {input_text}\nAgent:", max_length=100, do_sample=True, top_k=50)[0]["generated_text"]
        return generated_response
    except Exception as e:
        return f"Error loading model: {e}"

def terminal_interface(command: str, project_name: str = None) -> str:
    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, shell=True, capture_output=True, text=True, cwd=project_path)
    else:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
    return result.stdout

def code_editor_interface(code: str) -> str:
    try:
        formatted_code = black.format_str(code, mode=black.FileMode())
    except black.NothingChanged:
        formatted_code = code

    result = StringIO()
    sys.stdout = result
    sys.stderr = result

    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
    sys.stdout = sys.__stdout__
    sys.stderr = sys.__stderr__

    lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()

    return formatted_code, lint_message

def summarize_text(text: str) -> str:
    summarizer = pipeline("summarization")
    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return summary[0]['summary_text']

def sentiment_analysis(text: str) -> str:
    analyzer = pipeline("sentiment-analysis")
    result = analyzer(text)
    return result[0]['label']

def translate_code(code: str, source_language: str, target_language: str) -> str:
    # Use a Hugging Face translation model instead of OpenAI
    # Example: English to Spanish
    translator = pipeline(
        "translation", model="bartowski/Codestral-22B-v0.1-GGUF")
    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
    return translated_code

def generate_code(code_idea: str, model_name: str) -> str:
    """Generates code using the selected model."""
    try:
        generator = pipeline('text-generation', model=model_name)
        generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
        return generated_code
    except Exception as e:
        return f"Error generating code: {e}"

def chat_interface(input_text: str) -> str:
    """Handles general chat interactions with the user."""
    # Use a Hugging Face chatbot model or your own logic
    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
    return response

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

def add_code_to_workspace(project_name: str, code: str, file_name: str) -> str:
    project_path = os.path.join(PROJECT_ROOT, project_name)
    if not os.path.exists(project_path):
        return f"Project '{project_name}' does not exist."

    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)
    return f"Code added to '{file_name}' in project '{project_name}'."

def create_space_on_hugging_face(api, name, description, public, files, entrypoint="launch.py"):
    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
    headers = {"Authorization": f"Bearer {api.access_token}"}
    payload = {
        "public": public,
        "gitignore_template": "web",
        "default_branch": "main",
        "archived": False,
        "files": []
    }
    for filename, contents in files.items():
        data = {
            "content": contents,
            "path": filename,
            "encoding": "utf-8",
            "mode": "overwrite"
        }
        payload["files"].append(data)
    response = requests.post(url, json=payload, headers=headers)
    response.raise_for_status()
    location = response.headers.get("Location")
    # wait_for_processing(location, api)  # You might need to implement this if it's not already defined

    return Repository(name=name, api=api)

# 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"):
        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, lint_message = code_editor_interface(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")

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)

        # Automatically create requirements.txt and app.py
        project_path = os.path.join(PROJECT_ROOT, project_name)
        requirements_file = os.path.join(project_path, "requirements.txt")
        if not os.path.exists(requirements_file):
            with open(requirements_file, "w") as f:
                f.write("# Add your project's dependencies here\n")

        app_file = os.path.join(project_path, "app.py")
        if not os.path.exists(app_file):
            with open(app_file, "w") as f:
                f.write("# Your project's main application logic goes here\n")

    # 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.session_state.terminal_history.append(
            (f"Add Code: {code_to_add}", add_code_status))
        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.session_state.terminal_history.append(
            (terminal_input, terminal_output))
        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}")

    # Code Generation
    st.subheader("Code Generation")
    code_idea = st.text_input("Enter your code idea:")

    # Model Selection Menu
    selected_model = st.selectbox(
        "Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)

    if st.button("Generate Code"):
        generated_code = generate_code(code_idea, selected_model)
        st.code(generated_code, language="python")

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

    # If everything went well, proceed to deploy the Space
    if agent._hf_api and agent.has_valid_hf_token():
        agent.deploy_built_space_to_hf()
        # Use the hf_token to interact with the Hugging Face API
        api = HfApi(token="hf_token")        # Function to create a Space on Hugging Face
        create_space_on_hugging_face(api, agent.name, agent.description, True, get_built_space_files())