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

# Replace st.secrets with os.environ
hf_token = os.environ.get("huggingface_token")

if not hf_token:
    st.error("Hugging Face API key not found. Please set the HUGGINGFACE_API_KEY environment variable.")
    st.stop()

# Rest of your code here
st.write("Hugging Face API key successfully loaded!")

# Rest of your code here
st.write("Hugging Face API key successfully loaded!")
# 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"
    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())