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import gradio as gr
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
SAVED_CHECKPOINT = 'mikegarts/distilgpt2-erichmariaremarque'
MIN_WORDS = 100
def get_model():
model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)
return model, tokenizer
def generate(prompt):
model, tokenizer = get_model()
input_context = prompt
input_ids = tokenizer.encode(input_context, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids=input_ids,
max_length=100,
temperature=0.7,
num_return_sequences=3,
do_sample=True,
# forced_eos_token_id=tokenizer.encode('.')[0]
)
return tokenizer.decode(outputs[0], skip_special_tokens=True).rsplit('.', 1)[0] + '.'
def predict(prompt):
return generate(prompt=prompt)
title = "What would Remarques say?"
description = """
The bot was trained to complete your prompt as if it was a begining of a paragraph of Remarque's book.
<img src="https://upload.wikimedia.org/wikipedia/commons/1/10/Bundesarchiv_Bild_183-R04034%2C_Erich_Maria_Remarque_%28cropped%29.jpg" align=center width=200px>
"""
gr.Interface(
fn=predict,
inputs="textbox",
outputs="text",
title=title,
description=description,
examples=[["I was drinking because"], ["Who is Karl for me?"], ["My most brutal mistake was"]]
).launch(debug=True)
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