app.py

import pickle
import torch
import gradio as gr
from gpt import GPTLanguageModel


with open('stoi_itos.pkl', 'rb') as file:
    stoi, itos = pickle.load(file)

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
lm = GPTLanguageModel()
lm.load_state_dict(torch.load('shakespeare_lm.pt', map_location='cpu'))
lm.eval()
  

def inference(prompt: str):
    encoded_prompt = torch.tensor(encode(prompt), dtype=torch.long).unsqueeze(0) 
    output = decode(lm.generate(encoded_prompt, max_new_tokens=500)[0].tolist())
    return output

gr_interface = gr.Interface(
    inference,
    inputs=[
        gr.Textbox("man walking on the streets", label="Prompt"),
    ],
    outputs=[
        gr.Textbox(
            label="Generated story",
            height="auto",
        )
    ],
    title="Stories generated by a language model trained on Shakespeare's work",
    examples=[
        ["Sunrise rising"],
        ["A big blast sound"]
    ]
)
gr_interface.launch(debug=True)