import os
import subprocess
import random
import json
from datetime import datetime
import gradio as gr  # Corrected import for gradio

class App(gr.Blocks):  # Corrected class inheritance
    def __init__(self):
        super().__init__()
        self.app_state = {"components": []}
        self.terminal_history = ""
        self.components_registry = {
            "Button": {
                "properties": {
                    "label": "Click Me",
                    "onclick": ""
                },
                "description": "A clickable button",
                "code_snippet": "gr.Button(value='{{label}}', variant='primary')"
            },
            # ... Other component definitions
        }
        self.nlp_model_names = [
            "google/flan-t5-small",
            # ... Other NLP model names
        ]
        self.nlp_models = []
        # self.initialize_nlp_models()  # Moved to run() for Gradio
        self.exited = False  # Add the missing attribute

    def initialize_nlp_models(self):
        for nlp_model_name in self.nlp_model_names:
            try:
                # Assuming the use of transformers library for NLP models
                from transformers import pipeline
                model = pipeline('text-generation', model=nlp_model_name)
                self.nlp_models.append(model)
            except Exception as e:
                print(f"Failed to load model {nlp_model_name}: {e}")
                self.nlp_models.append(None)

    def get_nlp_response(self, input_text, model_index):
        if self.nlp_models[model_index]:
            response = self.nlp_models[model_index](input_text)
            return response[0]['generated_text']
        else:
            return "NLP model not available."

    class Component:
        def __init__(self, type, properties=None, id=None):
            self.type = type
            self.properties = properties or {}
            self.id = id or f"{type}_{random.randint(1000, 9999)}"

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

        def render(self):
            code_snippet = self.properties.get("code_snippet", "")
            for key, value in self.properties.items():
                code_snippet = code_snippet.replace(f"{{{{{key}}}}}", str(value))
            return code_snippet

    def update_app_canvas(self):
        code = ""
        for component in self.app_state["components"]:
            code += component.render() + "\n"
        return code

    def add_component(self, component_type):
        if component_type in self.components_registry:
            component = self.Component(
                type=component_type,
                properties=self.components_registry[component_type]["properties"]
            )
            self.app_state["components"].append(component)
            # self.update_app_canvas()  # Updated to return code
        else:
            print(f"Component type {component_type} not found in registry.")

    def run_terminal_command(self, command, history):
        try:
            result = subprocess.run(command, shell=True, capture_output=True, text=True)
            history += result.stdout + result.stderr
            return history
        except Exception as e:
            return str(e)

    def compress_history(self, history):
        lines = history.split('\\n')
        compressed_lines = [line for line in lines if not line.strip().startswith('#')]
        return '\\n'.join(compressed_lines)

    def understand_test_results(self, test_results):
        # Placeholder for understanding test results
        return "Test results understood."

    def get_help_message(self):
        return "Available commands: add_component, run_terminal_command, compress_history, understand_test_results, get_help_message"

    def process_input(self, input_text):
        if input_text.startswith("add_component"):
            _, component_type = input_text.split()
            self.add_component(component_type)
            return self.update_app_canvas()  # Return updated code
        elif input_text.startswith("run_terminal_command"):
            _, command = input_text.split(maxsplit=1)
            self.terminal_history = self.run_terminal_command(command, self.terminal_history)
            return self.terminal_history
        elif input_text.startswith("compress_history"):
            self.terminal_history = self.compress_history(self.terminal_history)
            return self.terminal_history
        elif input_text.startswith("understand_test_results"):
            _, test_results = input_text.split(maxsplit=1)
            return self.understand_test_results(test_results)
        elif input_text == "get_help_message":
            return self.get_help_message()
        else:
            return "Unknown command. Type 'get_help_message' for available commands."

    def run(self):
        self.initialize_nlp_models()  # Initialize NLP models here
        with gr.Blocks() as demo:
            input_text = gr.Textbox(label="Enter your command:")
            output_text = gr.Textbox(label="Output:")
            btn = gr.Button("Run")
            btn.click(self.process_input, inputs=[input_text], outputs=[output_text])
        demo.launch()

if __name__ == "__main__":
    app = App()
    app.run()