import subprocess
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
import gradio as gr
import os
import json
import streamlit as st
from streamlit_ace import st_ace
from agent import (
    PREFIX,
    ACTION_PROMPT,
    SEARCH_QUERY,
    TASK_PROMPT,
    READ_PROMPT,
    ADD_PROMPT,
    MODIFY_PROMPT,
    UNDERSTAND_TEST_RESULTS,
    COMPRESS_HISTORY,
    LOG_PROMPT,
    LOG_RESPONSE,
)
import importlib
import sys

def initialize_global_variables():
    global HUGGING_FACE_REPO_URL, PROJECT_ROOT, AGENT_DIRECTORY, GRADIO_SERVER_PORT
    HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/CodeMixt"
    PROJECT_ROOT = "projects"
    AGENT_DIRECTORY = "agents"
    GRADIO_SERVER_PORT = 7860  # Choose a consistently unused port

initialize_global_variables()

# Initialize session state attributes
for attr in ['chat_history', 'terminal_history', 'workspace_projects', 'available_agents', 'current_state']:
    if attr not in st.session_state:
        st.session_state[attr] = []

def save_agent_to_file(agent):
    agents_path = os.path.join(PROJECT_ROOT, AGENT_DIRECTORY)
    if not os.path.exists(agents_path):
        os.makedirs(agents_path)

    agent_file = os.path.join(agents_path, f"{agent.name}.txt")
    config_file = os.path.join(agents_path, f"{agent.name}Config.txt")

    with open(agent_file, "w") as file:
        file.write(agent.create_agent_prompt())

    with open(config_file, "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):
    agent_file = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
    if os.path.exists(agent_file):
        with open(agent_file, "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()

# Global Variables
terminal_history = ""

# Component Library
components_registry = {
    "Button": {
        "properties": {"label": "Click Me", "onclick": ""},
        "description": "A clickable button",
        "code_snippet": 'gr.Button(value="{label}", variant="primary")',
    },
    "Text Input": {
        "properties": {"value": "", "placeholder": "Enter text"},
        "description": "A field for entering text",
        "code_snippet": 'gr.Textbox(label="{placeholder}")',
    },
    "Image": {
        "properties": {"src": "#", "alt": "Image"},
        "description": "Displays an image",
        "code_snippet": 'gr.Image(label="{alt}")',
    },
    "Dropdown": {
        "properties": {"choices": ["Option 1", "Option 2"], "value": ""},
        "description": "A dropdown menu for selecting options",
        "code_snippet": 'gr.Dropdown(choices={choices}, label="Dropdown")',
    },
    # Add more components here...
}

# NLP Model (Example using Hugging Face)
nlp_model_names = [
    "google/flan-t5-small",
    "Qwen/CodeQwen1.5-7B-Chat-GGUF",
    "bartowski/Codestral-22B-v0.1-GGUF",
    "bartowski/AutoCoder-GGUF"
]
nlp_models = []

for nlp_model_name in nlp_model_names:
    try:
        cached_download(hf_hub_url(nlp_model_name, revision="main"))
        nlp_models.append(InferenceClient(nlp_model_name))
    except:
        nlp_models.append(None)

# Function to get NLP model response
def get_nlp_response(input_text, model_index):
    if nlp_models[model_index]:
        response = nlp_models[model_index].text_generation(input_text)
        return response.generated_text
    else:
        return "NLP model not available."

# Component Class
class Component:
    def __init__(self, type, properties=None, id=None):
        self.id = id or random.randint(1000, 9999)
        self.type = type
        self.properties = properties or components_registry[type]["properties"].copy()

    def to_dict(self):
        return {
            "id": self.id,
            "type": self.type,
            "properties": self.properties,
        }

    def render(self):
        # Properly format choices for Dropdown
        if self.type == "Dropdown":
            self.properties["choices"] = (
                str(self.properties["choices"])
                .replace("[", "")
                .replace("]", "")
                .replace("'", "")
            )
        return components_registry[self.type]["code_snippet"].format(**self.properties)

# App Creation Process Class
class AppCreationProcess:
    def __init__(self):
        self.current_step = 1
        self.app_name = ""
        self.components = []

    def get_current_step_info(self):
        steps = {
            1: "App Initialization",
            2: "Component Addition",
            3: "Property Configuration",
            4: "Code Generation",
            5: "Deployment"
        }
        return f"Step {self.current_step}: {steps[self.current_step]}"

    def add_component(self, component_type):
        new_component = Component(component_type)
        self.components.append(new_component.to_dict())
        return self.update_app_canvas()

    def set_component_property(self, component_id, property_name, property_value):
        for component in self.components:
            if component['id'] == component_id:
                if property_name in component['properties']:
                    component['properties'][property_name.strip()] = property_value.strip()
                    return self.update_app_canvas(), f"Property '{property_name}' set to '{property_value}' for component {component_id}"
                else:
                    return None, f"Error: Property '{property_name}' not found in component {component_id}"
        return None, f"Error: Component with ID {component_id} not found."

    def update_app_canvas(self):
        components_html = "".join([
            f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>"
            for component in self.components
        ])
        return components_html

    def generate_python_code(self):
        code = f"""import gradio as gr\n\nwith gr.Blocks() as {self.app_name}:\n"""
        for component in self.components:
            code += "    " + Component(**component).render() + "\n"
        code += f"\n{self.app_name}.launch()\n"
        return code

    def deploy_to_huggingface(self):
        # Generate Python code
        code = self.generate_python_code()
        # Create requirements.txt
        with open("requirements.txt", "w") as f:
            f.write("gradio==3.32.0\n")
        # Create the app.py file
        with open("app.py", "w") as f:
            f.write(code)
        # Execute the deployment command
        try:
            subprocess.run(["huggingface-cli", "repo", "create", "--type", "space", "--space_sdk", "gradio", self.app_name], check=True)
            subprocess.run(["git", "init"], cwd=f"./{self.app_name}", check=True)
            subprocess.run(["git", "add", "."], cwd=f"./{self.app_name}", check=True)
            subprocess.run(["git", "commit", "-m", "Initial commit"], cwd=f"./{self.app_name}", check=True)
            subprocess.run(["git", "push", "https://huggingface.co/spaces/" + self.app_name, "main"], cwd=f"./{self.app_name}", check=True)
            return f"Successfully deployed to Hugging Face Spaces: https://huggingface.co/spaces/{self.app_name}"
        except Exception as e:
            return f"Error deploying to Hugging Face Spaces: {e}"

app_process = AppCreationProcess()

# Function to handle terminal input
def run_terminal_command(command, history):
    global terminal_history
    output = ""
    try:
        # Basic command parsing (expand with NLP)
        if command.startswith("add "):
            component_type = command.split("add ", 1)[1].strip()
            output = app_process.add_component(component_type)
        elif command.startswith("set "):
            _, output = set_component_property(command)
        elif command.startswith("search "):
            search_query = command.split("search ", 1)[1].strip()
            output = i_s(search_query)
        elif command.startswith("deploy "):
            output = app_process.deploy_to_huggingface()
        else:
            # Attempt to execute command as Python code
            try:
                result = subprocess.check_output(
                    command, shell=True, stderr=subprocess.STDOUT, text=True
                )
                output = result
            except Exception as e:
                output = f"Error executing Python code: {str(e)}"
    except Exception as e:
        output = f"Error: {str(e)}"
    finally:
        terminal_history += f"User: {command}\n{output}\n"
    return terminal_history

def set_component_property(command):
    try:
        # Improved 'set' command parsing
        set_parts = command.split(" ", 2)[1:]
        if len(set_parts) != 2:
            raise ValueError("Invalid 'set' command format.")
        component_id = int(set_parts[0])  # Use component ID
        property_name, property_value = set_parts[1].split("=", 1)
        return app_process.set_component_property(component_id, property_name, property_value)
    except Exception as e:
        return None, f"Error: {str(e)}\n"

# Function to handle chat interaction
def run_chat(message, history):
    global terminal_history
    if message.startswith("!"):
        command = message[1:]
        terminal_history = run_terminal_command(command, history)
    else:
        model_index = 0  # Select the model to use for chat response
        response = get_nlp_response(message, model_index)
        if response:
            return history, terminal_history + f"User: {message}\nAssistant: {response}"
        else:
            return history, terminal_history + f"User: {message}\nAssistant: I'm sorry, I couldn't generate a response. Please try again.\n"

# Gradio Interface
with gr.Blocks() as iface:
    gr.Markdown("# Sequential App Builder")
    
    with gr.Row():
        current_step = gr.Markdown(app_process.get_current_step_info())
    
    with gr.Row():
        prev_button = gr.Button("Previous Step")
        next_button = gr.Button("Next Step")

    # Step 1: App Initialization
    with gr.Group() as step1:
        app_name_input = gr.Textbox(label="Enter App Name")
        init_app_button = gr.Button("Initialize App")

    # Step 2: Component Addition
    with gr.Group() as step2:
        component_type = gr.Dropdown(choices=list(components_registry.keys()), label="Select Component Type")
        add_component_button = gr.Button("Add Component")
        components_display = gr.HTML()

    # Step 3: Property Configuration
    with gr.Group() as step3:
        component_id = gr.Number(label="Component ID")
        property_name = gr.Textbox(label="Property Name")
        property_value = gr.Textbox(label="Property Value")
        set_property_button = gr.Button("Set Property")

    # Step 4: Code Generation
    with gr.Group() as step4:
        generated_code = gr.Code(language="python")
        generate_code_button = gr.Button("Generate Code")

    # Step 5: Deployment
    with gr.Group() as step5:
        deploy_button = gr.Button("Deploy to Hugging Face Spaces")
        deployment_status = gr.Markdown()

    # Chat and Terminal (optional, can be hidden or shown based on preference)
    with gr.Accordion("Advanced", open=False):
        chat_history = gr.Chatbot(label="Chat with Agent")
        chat_input = gr.Textbox(label="Your Message")
        chat_button = gr.Button("Send")
        
        terminal_output = gr.Textbox(lines=8, label="Terminal", value=terminal_history)
        terminal_input = gr.Textbox(label="Enter Command")
        terminal_button = gr.Button("Run")

# App Creation Process Class
class AppCreationProcess:
    def __init__(self):
        self.current_step = 1
        self.app_name = ""
        self.components = []

    def get_current_step_info(self):
        steps = {
            1: "App Initialization",
            2: "Component Addition",
            3: "Property Configuration",
            4: "Code Generation",
            5: "Deployment"
        }
        
def next_step():
    app_process.next_step()
    current_step_info = app_process.get_current_step_info()
    visibility_updates = update_visibility(app_process.current_step)
    
    # Unpack the visibility updates
    step1_update = visibility_updates[step1]
    step2_update = visibility_updates[step2]
    step3_update = visibility_updates[step3]
    step4_update = visibility_updates[step4]
    step5_update = visibility_updates[step5]
    
    return [
        current_step_info,  # This should be a string for the Markdown component
        step1_update,
        step2_update,
        step3_update,
        step4_update,
        step5_update
    ]
def prev_step():
    app_process.previous_step()
    return app_process.get_current_step_info(), update_visibility(app_process.current_step)
    next_button.click(next_step, outputs=[current_step, step1, step2, step3, step4, step5])
    prev_button.click(prev_step, outputs=[current_step, step1, step2, step3, step4, step5])

    # Step 1: Initialize App
def init_app(name):
    app_process.app_name = name
    return f"App '{name}' initialized."
    init_app_button.click(init_app, inputs=[app_name_input], outputs=[components_display])
    # Step 2: Add Component
    add_component_button.click(app_process.add_component, inputs=[component_type], outputs=[components_display])
    # Step 3: Set Property
    set_property_button.click(app_process.set_component_property, inputs=[component_id, property_name, property_value], outputs=[components_display])
    # Step 4: Generate Code
    generate_code_button.click(app_process.generate_python_code, outputs=[generated_code])
    # Step 5: Deploy
    deploy_button.click(app_process.deploy_to_huggingface, outputs=[deployment_status])
    # Existing chat and terminal functionality
    chat_button.click(run_chat, inputs=[chat_input, chat_history], outputs=[chat_history, terminal_output])
    terminal_button.click(run_terminal_command, inputs=[terminal_input, terminal_output], outputs=[terminal_output])

iface.launch(server_port=GRADIO_SERVER_PORT)