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import os | |
import asyncio | |
from concurrent.futures import ThreadPoolExecutor | |
import requests | |
import gradio as gr | |
MAX_NEW_TOKENS = 128 | |
TOKEN = os.environ.get("API_TOKEN", None) | |
URLS = [ | |
"https://api-inference.huggingface.co/models/google/flan-ul2", | |
"https://api-inference.huggingface.co/models/google/flan-t5-xxl", | |
] | |
def fetch(session, text, api_url): | |
model = api_url.split("/")[-1] | |
response = session.post(api_url, json={"inputs": text, "parameters": {"max_new_tokens": MAX_NEW_TOKENS}}) | |
if response.status_code != 200: | |
return model, None | |
return model, response.json() | |
examples = [ | |
["Please answer to the following question. Who is going to be the next Ballon d'or?"], | |
["Q: Can Barack Obama have a conversation with George Washington? Give the rationale before answering."], | |
[ | |
"Summarize the following text: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital. Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well. Therefore, Peter stayed with her at the hospital for 3 days without leaving." | |
], | |
["Please answer the following question: What is the boiling point of water?"], | |
["Answer the following question by detailing your reasoning: Are Pokemons alive?"], | |
["Translate to German: How old are you?"], | |
["Generate a cooking recipe to make bolognese pasta:"], | |
["Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"], | |
[ | |
"Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" | |
], | |
[ | |
"Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch and bought 6 more, how many apples do they have?" | |
], | |
[ | |
"""Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? | |
A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11. | |
Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?""" | |
], | |
] | |
title = "Flan UL2 vs Flan T5 XXL" | |
description = "This demo compares [Flan-T5-xxl](https://huggingface.co/google/flan-t5-xxl) and [Flan-UL2](https://huggingface.co/google/flan-ul2). Learn more about these models in their model card!" | |
async def inference(text): | |
with ThreadPoolExecutor(max_workers=2) as executor: | |
with requests.Session() as session: | |
session.headers = {"Authorization": f"Bearer {TOKEN}"} | |
# Initialize the event loop | |
loop = asyncio.get_event_loop() | |
tasks = [ | |
loop.run_in_executor( | |
executor, fetch, *(session, text, url) # Allows us to pass in multiple arguments to `fetch` | |
) | |
for url in URLS | |
] | |
# Initializes the tasks to run and awaits their results | |
responses = [None, None] | |
for (model, response) in await asyncio.gather(*tasks): | |
if response is not None: | |
if model == "flan-ul2": | |
responses[0] = response[0]["generated_text"] | |
elif model == "flan-t5-xxl": | |
responses[1] = response[0]["generated_text"] | |
return responses | |
io = gr.Interface( | |
inference, | |
gr.Textbox(lines=3), | |
outputs=[gr.Textbox(lines=3, label="Flan T5-UL2"), gr.Textbox(lines=3, label="Flan T5-XXL")], | |
title=title, | |
description=description, | |
examples=examples, | |
) | |
io.launch() | |