Spaces:
Runtime error
Runtime error
File size: 4,204 Bytes
876e501 5d5ce1d 876e501 5d5ce1d 876e501 5d5ce1d 876e501 9b28fe7 876e501 9b28fe7 876e501 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
import gradio as gr
from huggingface_hub import snapshot_download
import os # utility library
# libraries to load the model and serve inference
import tensorflow_text
import tensorflow as tf
CACHE_DIR = "hfhub_cache" # where the library's fork would be stored once downloaded
if not os.path.exists(CACHE_DIR):
os.mkdir(CACHE_DIR)
# download the files from huggingface repo and load the model with tensorflow
snapshot_download(repo_id="stevekola/T5", cache_dir=CACHE_DIR)
saved_model_path = os.path.join(CACHE_DIR, os.listdir(CACHE_DIR)[0])
model = tf.saved_model.load(saved_model_path, ["serve"])
title = "Interactive demo: T5 Multitasking Demo"
description = "Demo for T5's different tasks including machine translation, \
text summarization, document similarity, and grammatical correctness of sentences."
def predict_fn(x):
"""Function to get inferences from model on live data points.
params:
x input text to run get output on
returns:
a numpy array representing the output
"""
return model.signatures['serving_default'](tf.constant(x))['outputs'].numpy()
def predict(task_type, sentence):
"""Function to parse the user inputs, run the parsed text through the
model and return output in a readable format.
params:
task_type sentence representing the type of task to run on T5 model
sentence sentence to get inference on
returns:
text decoded into a human-readable format.
"""
task_dict = {
"Translate English to French": "Translate English to French",
"Translate English to German": "Translate English to German",
"Translate English to Romanian": "Translate English to Romanian",
"Grammatical Correctness of Sentence": "cola sentence",
"Text Summarization": "summarize",
"Document Similarity Score (separate the 2 sentences with 3 dashes `---`)": "stsb",
}
question = f"{task_dict[task_type]}: {sentence}" # parsing the user inputs into a format recognized by T5
# Document Similarity takes in two sentences so it has to be parsed in a separate manner
if task_type.startswith("Document Similarity"):
sentences = sentence.split('---')
question = f"{task_dict[task_type]} sentence1: {sentences[0]} sentence2: {sentences[1]}"
return predict_fn([question])[0].decode('utf-8')
iface = gr.Interface(fn=predict,
inputs=[gr.inputs.Radio(
choices=["Translate English to French",
"Translate English to German",
"Translate English to Romanian",
"Grammatical Correctness of Sentence",
"Text Summarization",
"Document Similarity Score (separate the 2 sentences with 3 dashes `---`)"],
label="Task Type"
),
gr.inputs.Textbox(label="Sentence")],
outputs="text",
title=title,
description=description,
examples=[
["Translate English to French", "I am Steven and I live in Lagos, Nigeria"],
["Translate English to German", "I am Steven and I live in Lagos, Nigeria"],
["Translate English to Romanian", "I am Steven and I live in Lagos, Nigeria"],
["Grammatical Correctness of Sentence", "I am Steven and I live in Lagos, Nigeria"],
["Text Summarization",
"I don't care about those doing the comparison, but comparing the Ghanaian Jollof Rice to \
Nigerian Jollof Rice is an insult to Nigerians"],
["Document Similarity Score (separate the 2 sentences with 3 dashes `---`)",
"I reside in the commercial capital city of Nigeria, which is Lagos---I live in Lagos"],
],
theme="huggingface",
enable_queue=True)
iface.launch()
|