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""" Modified from https://huggingface.co/spaces/flax-community/gpt2-indonesian/tree/main """
import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
description = """
## Overview
* **Overall Result:** Eval loss : 1.45, Eval Perplexity : 3.141
* **Data:** [mC4-bn](https://huggingface.co/datasets/mc4)
* **Train Steps:** 250k steps
* **Contributors:** Khalid Saifullah, Tasmiah Tahsin Mayeesha, Ritobrata Ghosh, Ibrahim Musa, M Saiful Bari
* **link** [🤗 flax-community/gpt2-bengali](https://huggingface.co/flax-community/gpt2-bengali/)
"""
headers = {}
MODELS = {
"GPT-2 Bengali": {
"url": "https://api-inference.huggingface.co/models/flax-community/gpt2-bengali"
},
"GPT-2 Finetuned(On Bengali Songs)": {
"url": "https://api-inference.huggingface.co/models/khalidsaifullaah/bengali-lyricist-gpt2"
},
}
def query(payload, model_name):
data = json.dumps(payload)
print("model url:", MODELS[model_name]["url"])
response = requests.request("POST", MODELS[model_name]["url"], headers=headers, data=data)
return json.loads(response.content.decode("utf-8"))
def process(text: str,
model_name: str,
max_len: int,
temp: float,
top_k: int,
top_p: float):
payload = {
"inputs": text,
"parameters": {
"max_new_tokens": max_len,
"top_k": top_k,
"top_p": top_p,
"temperature": temp,
"repetition_penalty": 2.0,
},
"options": {
"use_cache": True,
}
}
return query(payload, model_name)
st.set_page_config(page_title="Bengali GPT-2 Demo")
st.title("Bengali GPT-2")
st.sidebar.subheader("Configurable parameters")
max_len = st.sidebar.number_input(
"Maximum length",
value=30,
help="The maximum length of the sequence to be generated."
)
temp = st.sidebar.slider(
"Temperature",
value=1.0,
min_value=0.1,
max_value=100.0,
help="The value used to module the next token probabilities."
)
top_k = st.sidebar.number_input(
"Top k",
value=10,
help="The number of highest probability vocabulary tokens to keep for top-k-filtering."
)
top_p = st.sidebar.number_input(
"Top p",
value=0.95,
help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation."
)
do_sample = st.sidebar.selectbox('Sampling?', (True, False), help="Whether or not to use sampling; use greedy decoding otherwise.")
st.markdown(
"""Bengali GPT-2 demo. Part of the [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/). Also features a finetuned version on bengali song lyrics."""
)
st.write(description)
model_name = st.selectbox('Model',(['GPT-2 Bengali', 'GPT-2 Finetuned(On Bengali Songs)']))
ALL_PROMPTS = list(PROMPT_LIST.keys())+["Custom"]
prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)
if prompt == "Custom":
prompt_box = "Enter your text here"
else:
prompt_box = random.choice(PROMPT_LIST[prompt])
text = st.text_area("Enter text", prompt_box)
if st.button("Run"):
with st.spinner(text="Getting results..."):
st.subheader("Result")
print(f"maxlen:{max_len}, temp:{temp}, top_k:{top_k}, top_p:{top_p}")
result = process(text=text,
model_name=model_name,
max_len=int(max_len),
temp=temp,
top_k=int(top_k),
top_p=float(top_p))
print("result:", result)
if "error" in result:
if type(result["error"]) is str:
st.write(f'{result["error"]}.', end=" ")
if "estimated_time" in result:
st.write(f'Please try it again in about {result["estimated_time"]:.0f} seconds')
else:
if type(result["error"]) is list:
for error in result["error"]:
st.write(f'{error}')
else:
result = result[0]["generated_text"]
st.write(result.replace("\
", " \
"))
st.text("English translation")
st.write(translate(result, "en", "bn").replace("\
", " \
"))
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