import gradio as gr
import os


class ContentAgentUI:
    def __init__(self):
        # Set the path to the external CSS file
        css_path = os.path.join(os.getcwd(), "ui", "styles.css")
        
        self.ca_gui = gr.Blocks(css=css_path)
        #self.ca_gui = gr.Blocks()
        self.sections = [
            self.create_header,
            self.create_user_guidance,
            self.create_main,
            self.create_examples,
            self.create_footer,
        ]
        
        for section in self.sections:
            section()
        
        self.ca_gui.launch()

    def create_header(self):
        agent_header = """
        #Content Agent                
        """
        with self.ca_gui:
            gr.Markdown(agent_header)

    def create_user_guidance(self):
        guidance = """
        Please enter text below to get started. The AI Agent will try to determine whether the language is polite and uses the following classification:
        - `polite`
        - `somewhat polite`
        - `neutral`
        - `impolite`
        App is running `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B` text generation model.
        Uses Intel's Polite Guard NLP library.
        Compute is GCP · Nvidia L4 · 4x GPUs · 96 GB
        """
        with self.ca_gui:
            gr.Markdown(guidance)
            
    def create_main(self):
        with self.ca_gui:
            with gr.Row():
                with gr.Column():
                    self.user_input = gr.Textbox(label="Your Input", placeholder="Enter something here...")
                    self.submit_button = gr.Button("Submit")
                    self.output = gr.Textbox(label="Content feedback", interactive=False, lines=10, max_lines=20 )
                    
                    # Define the function to be called when the button is clicked or Enter is pressed
                    self.submit_button.click(process_input, inputs=self.user_input, outputs=self.output)
                    self.user_input.submit(process_input, inputs=self.user_input, outputs=self.output)

 
    # Function to generate predefined examples
    def get_example():
        # Define the path to the 'examples' directory
        example_root = os.path.join(os.path.dirname(__file__), "examples")
        
        # Get list of all example text file paths
        example_files = [os.path.join(example_root, _) for _ in os.listdir(example_root) if _.endswith("txt")]
        
        # Read the content of each file (assuming they're plain text files)
        examples = []
        
        for file_path in example_files:
            example_content = ""
            with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
                example_content = f.read()
            
            examples.append(example_content)  # Append the content to the list
    
        return examples

    def create_examples(self):
        # Fetch examples by calling get_example() here
        examples = get_example()
        print("examples")
        print(examples)

        example_radio = gr.Radio(choices=examples, label="Try one of these examples:")
        
        # When an example is selected, populate the input field
        with self.ca_gui:
            example_radio.change(fn=lambda example: example, inputs=example_radio, outputs=self.user_input)

    def create_footer(self):
        with self.ca_gui:
            gr.Markdown("<div id='footer'>Thanks for trying it out!</div>")


    
    # Function for Main content (takes user input and returns a response)
    def process_input(input_text):
    #return f"You entered: {user_input}"

    #def get_agent_response(input_text):
        try:
            # Pass the input to the agent
            output = agent.get_response(input_text)
            
            # Return the agent's response
            return output
        except Exception as e:
            # Handle any errors that occur
            return f"Error: {str(e)}"
    
        self.user_input.change(
            fn=get_agent_response,
            inputs=self.user_input,
            outputs=self.output
        )

    def pass_through_agent(self, agent):
        # Simulate the agent's response
        agent_response = agent(self.user_input.value)
        self.output.update(agent_response)

        # Pass the input to the agent
        output = agent.get_response(input_text)

        # Update the output text box with the agent's response
        self.submit_button.click(
            fn=process_input,
            inputs=self.user_input,
            outputs=self.output
        )
    
        self.user_input.submit(
            fn=get_agent_response,
            inputs=self.user_input,
            outputs=self.output
        )