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import gradio as gr
import torch
from torch import nn
import lightning.pytorch as pl
from torch.nn import functional as F
from utils import GPTLM,encode,decode
newmodel = GPTLM.load_from_checkpoint('shakespeare_gpt.pth')
def generate_dialogue(character_dropdown):
if character_dropdown == "NONE":
context = torch.zeros((1, 1), dtype=torch.long)
return decode(newmodel.model.generate(context, max_new_tokens=100)[0].tolist())
else:
context = torch.tensor([encode(character_dropdown)], dtype=torch.long)
output_dialogue = decode(newmodel.model.generate(context, max_new_tokens=100)[0].tolist())
# remove extra dialogue returned
output_dialogue = str(output_dialogue.split("\n\n")[0])
return output_dialogue
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>SHAKESPEARE DIALOGUE GENERATOR</h1>
<p>Generate dialogue for Shakespearean character by selecting character from dropdown.</p>
<p>Model: GPT, Dataset: Tiny Shakespeare, Token limit: 100.</p>
"""
with gr.Blocks(theme=gr.themes.Glass(),css=".gradio-container {background: url('file=https://github.com/Delve-ERAV1/S20/assets/11761529/c0ff84a4-dde6-473e-a820-d3797040eb9d')}") 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("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
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():
character_dropdown = gr.Dropdown(
label="Select a Character",
choices=["NONE","ROMEO","JULIET","MENENIUS","ANTONIO"],
value='Dream'
)
outputs = gr.Textbox(
label="Generated Dialogue"
)
inputs = [character_dropdown]
with gr.Column():
button = gr.Button("Generate")
button.click(generate_dialogue, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
interface.launch(enable_queue=True) |