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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
newmodel = GPTLM.load_from_checkpoint('shakespeare_gpt.pth')
chars = ['\n', ' ', '!', '$', '&', "'", ',', '-', '.', '3', ':', ';', '?', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
vocab_size = len(chars)
# create a mapping from characters to integers
stoi = { ch:i for i,ch in enumerate(chars) }
itos = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
def generate_dialogue(character_dropdown, seed_slider):
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)
return decode(newmodel.model.generate(context, max_new_tokens=100)[0].tolist())
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>
"""
with gr.Blocks(theme=gr.themes.Glass(),css=".gradio-container {background: url('file=https://github.com/santule/ERA/assets/20509836/b6b4031a-265d-43f6-bd59-813097c0022b')}") as interface:
gr.HTML(value=HTML_TEMPLATE, show_label=False)
with gr.Row():
character_dropdown = gr.Dropdown(
label="Select a Character",
choices=["NONE","ROMEO","JULIET","MENENIUS","ANTONIO"],
value='Dream'
)
seed_slider = gr.Slider(
label="Random Seed",
minimum=0,
maximum=1000,
step=1,
value=42
)
inputs = [character_dropdown, seed_slider]
with gr.Row():
outputs = gr.Textbox(
label="Generated Dialogue"
)
with gr.Row():
button = gr.Button("Generate Dialogue")
button.click(generate_dialogue, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
interface.launch(enable_queue=True) |