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| import torch | |
| import gradio as gr | |
| from utils import * | |
| from torch import nn | |
| import lightning.pytorch as pl | |
| from torch.nn import functional as F | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| HTML_TEMPLATE = """ | |
| <style> | |
| #app-header { | |
| text-align: center; | |
| background: rgba(255, 255, 255, 0.3); /* Semi-transparent white */ | |
| padding: 20px; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| position: relative; /* To position the artifacts */ | |
| } | |
| #app-header h1 { | |
| color: #FF0000; | |
| font-size: 2em; | |
| margin-bottom: 10px; | |
| } | |
| .concept { | |
| position: relative; | |
| transition: transform 0.3s; | |
| } | |
| .concept:hover { | |
| transform: scale(1.1); | |
| } | |
| .concept img { | |
| width: 100px; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| } | |
| .concept-description { | |
| position: absolute; | |
| bottom: -30px; | |
| left: 50%; | |
| transform: translateX(-50%); | |
| background-color: #4CAF50; | |
| color: white; | |
| padding: 5px 10px; | |
| border-radius: 5px; | |
| opacity: 0; | |
| transition: opacity 0.3s; | |
| } | |
| .concept:hover .concept-description { | |
| opacity: 1; | |
| } | |
| /* Artifacts */ | |
| </style> | |
| <div id="app-header"> | |
| <!-- Artifacts --> | |
| <div class="artifact large"></div> | |
| <div class="artifact large"></div> | |
| <div class="artifact large"></div> | |
| <div class="artifact large"></div> | |
| <!-- Content --> | |
| <h1>GPT NEXT WORD GENERATOR</h1> | |
| <p>Generate dialogue for given some initial prompt for context.</p> | |
| <p>Model: GPT, Dataset: arxiv + book + cc, Parameter Count: 160M</p> | |
| """ | |
| with gr.Blocks(theme=gr.themes.Glass(),css=".gradio-container {background: url('file=https://github.com/santule/ERA/assets/20509836/e78f2bb3-ddd8-4ce9-a941-3d3d7ef7a272')}") as interface: | |
| gr.HTML(value=HTML_TEMPLATE, show_label=False) | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| with gr.Row(): | |
| input_text = gr.Textbox( | |
| label="Input Text", | |
| value="Enter your prompt here: This text will set the context for the AI's response." | |
| ) | |
| temperature_dropdown = gr.Slider(0, 1, value=0.8, label="Temperature", info="Set the creativity level: Higher values produce more varied results, lower values generate more predictable text.") | |
| top_k_dropdown = gr.Slider(200, 300, value=200, label="Top K", info="Control the randomness: Limits the AI to consider only the top K most likely next words.") | |
| max_new_tokens = gr.Slider(10, 100, value=50, label="Max Tokens", info="Choose the length: This determines the maximum number of words the AI will generate.") | |
| outputs = gr.Textbox( | |
| label="Generated Dialogue" | |
| ) | |
| inputs = [input_text, temperature_dropdown, top_k_dropdown, max_new_tokens] | |
| with gr.Column(): | |
| button = gr.Button("Generate") | |
| button.click(generate_dialogue, inputs=inputs, outputs=outputs) | |
| # with gr.Row(): | |
| # gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=generate_dialogue, cache_examples=True,) | |
| interface.launch() |