from ailab_crs import NLP_tasks_crs, Prompt_engineering_crs
import gradio as gr
from googletrans import LANGUAGES 

supported_langs = list(LANGUAGES.values())



def main():
    nlp_tasks = NLP_tasks_crs()

    with gr.Blocks() as demo:
        gr.Markdown("# 🧠NaanhAI💡")
                
        with gr.Row():
            with gr.Column():
                text = gr.Textbox(label="Your Query", lines=8)
            with gr.Column():
                with gr.Accordion("Other Parameters For Translation or Summarization Tasks", open= True):
                    language = gr.Dropdown(choices=supported_langs, label="Select Target Language", value="english")
                    style = gr.Textbox(label="Choose Your Style", value = "polite")
                    # style = gr.Dropdown(choices= ["polite", "sad", "happy", "scientific", "religious"], value= "polite")
        
        with gr.Row(scale=5):
            with gr.Column(scale=1, min_width=1):
                btn = gr.Button("Q&A")
            with gr.Column(scale=2, min_width=1):
                btn1 = gr.Button("Translator")
            with gr.Column(scale=2, min_width=1):
                btn2 = gr.Button("Summarizer")
            with gr.Column(scale=2, min_width=1):
                btn3 = gr.Button("Translator_Summarizer")

        answer = gr.Textbox(label="AI Answer", lines=2)

        btn.click(
            fn= nlp_tasks.question_answer,
            inputs= text,
            outputs=answer
        )
        btn1.click(
            fn= nlp_tasks.translator,
            inputs= [text, language, style],
            outputs=answer
        )
        btn2.click(
            fn= nlp_tasks.summarization,
            inputs= text,
            outputs=answer
        )
        btn3.click(
            fn= nlp_tasks.translator_summarization,
            inputs= [text, language, style],
            outputs=answer
        )

    demo.launch(share=True)     
    # demo.launch(mcp_server=True)


if __name__ == "__main__":
    main()