import gradio as gr

def chat1(message,history):
    history = history or []
    message = message.lower()
    if message.startswith("how many"):
        response = ("1 to 10")
    else:
        response = ("whatever man")

    history.append((message, response))
    return history, history

chatbot = gr.Chatbot()
chatbot1 = gr.Chatbot()
chatbot2 = gr.Chatbot()

with gr.Blocks(
    title="",

) as demo:
    gr.Markdown("""
        <div style="overflow: hidden;color:#fff;display: flex;flex-direction: column;align-items: center; position: relative; width: 100%; height: 180px;background-size: cover; background-image: url(https://www.grssigns.co.uk/wp-content/uploads/web-Header-Background.jpg);">
            <img style="width: 130px;height: 60px;position: absolute;top:10px;left:10px" src="https://www.torontomu.ca/content/dam/tmumobile/images/TMU-Mobile-AppIcon.png"/>
            <span style="margin-top: 40px;font-size: 36px ;font-family:fantasy;">Efficient Fine tuning Of Large Language Models</span>
            <span style="margin-top: 10px;font-size: 14px;">By: Rahul Adams, Gerylyn Gao, Rajevan Lograjh & Mahir Faisal</span>
            <span style="margin-top: 5px;font-size: 14px;">Group Id: AR06 FLC: Alice Reuada</span>
        </div>
    """)
    with gr.Tab("Text Classification"):
        with gr.Row():
            gr.Markdown("<h1>Efficient Fine Tuning for Text Classification</h1>")
        with gr.Row():
            with gr.Column(scale=0.3):
                inp = gr.Textbox(placeholder="Prompt",label= "Enter Query")
                btn = gr.Button("Run")
                gr.Markdown("""
                            <p>Model: Tiny Bert <br>
                            NLP Task: Text Classification <br>
                            Dataset: IMDB Movie review dataset for sentiment analysis</p>
                            <p>insert information on training parameters here</p>
                            """)

            with gr.Row():
                with gr.Column():
                    out = gr.Textbox(label= " Untrained Model")
                with gr.Column():    
                    out1 = gr.Textbox(label= " Conventionaly Fine Tuned Model")
                with gr.Column():
                    out2 = gr.Textbox(label= " LoRA fine Tuned Model")

        btn.click(fn=chat1, inputs=inp, outputs=out)
        btn.click(fn=chat1, inputs=inp, outputs=out1)
        btn.click(fn=chat1, inputs=inp, outputs=out2)

    with gr.Tab("Natrual Language Infrencing"):
         with gr.Row():
             gr.Markdown("<h1>Efficient Fine Tuning for Natual Languae Infrencing</h1>")
         with gr.Row():
            with gr.Column(scale=0.3):
                inp = gr.Textbox(placeholder="Prompt",label= "Enter Query")
                btn = gr.Button("Run")
                gr.Markdown("""
                            <p>Model: ELECTRA Bert Small <br>
                            NLP Task: Natual Languae Infrencing <br>
                            Dataset: Stanford Natural Language Inference Dataset</p>
                            <p>insert information on training parameters here</p>
                            """)

            with gr.Row():
                with gr.Column():
                    out = gr.Textbox(label= " Untrained Model")
                with gr.Column():    
                    out1 = gr.Textbox(label= " Conventionaly Fine Tuned Model")
                with gr.Column():
                    out2 = gr.Textbox(label= " LoRA fine Tuned Model")

    with gr.Tab("Sematic Text Similarity"):
         with gr.Row():
             gr.Markdown("<h1>Efficient Fine Tuning for Semantic Text Similarity</h1>")
         with gr.Row():
            with gr.Column(scale=0.3):
                inp = gr.Textbox(placeholder="Prompt",label= "Enter Query")
                btn = gr.Button("Run")
                gr.Markdown("""
                            <p>Model: Tiny Bert <br>
                            NLP Task: Text Classification <br>
                            Dataset: IMDB Movie review dataset for sentiment analysis</p>
                            <p>insert information on training parameters here</p>
                            """)

            with gr.Row():
                with gr.Column():
                    out = gr.Textbox(label= " Untrained Model")
                with gr.Column():    
                    out1 = gr.Textbox(label= " Conventionaly Fine Tuned Model")
                with gr.Column():
                    out2 = gr.Textbox(label= " LoRA fine Tuned Model")

    with gr.Tab("More information"):
        gr.Markdown("stuff to add")


if __name__ == "__main__":
    demo.launch()