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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()