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import gradio as gr
from gpt import GPTLanguageModel
import torch
import config as cfg
torch.manual_seed(1337)
# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
with open('input.txt', 'r', encoding='utf-8') as f:
text = f.read()
# here are all the unique characters that occur in this text
chars = sorted(list(set(text)))
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
model = GPTLanguageModel(vocab_size)
model.load_state_dict(torch.load('saved_model.pth', map_location=cfg.device))
m = model.to(cfg.device)
def inference(input_context, count):
encoded_text = [encode(input_context)]
count = int(count)
context = torch.tensor(encoded_text, dtype=torch.long, device=cfg.device)
#print('--------------------context = ',context)
out_text = decode(m.generate(context, max_new_tokens=count)[0].tolist())
return out_text
title = "TSAI S21 Assignment: GPT training on mini shakespeare dataset"
description = "A simple Gradio interface that accepts a context and generates shakespere like text "
examples = [["Violets","200"],
["Julius","200"]
]
demo = gr.Interface(
inference,
inputs = [gr.Textbox(placeholder="Enter starting characters"), gr.Textbox(placeholder="Enter number of characters you want to generate")],
outputs = [gr.Textbox(label="Shakespeare like generated text")],
title = title,
description = description,
examples = examples
)
demo.launch()
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