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| 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() | |