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# Apache Software License 2.0
#
# Copyright (c) ZenML GmbH 2023. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from os.path import dirname
from typing import Optional
import click
import numpy as np
import sagemaker
from aws_helper import get_sagemaker_session
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from zenml.client import Client
import gradio as gr
@click.command()
@click.option(
"--tokenizer_name_or_path",
default=None,
help="Name or the path of the tokenizer.",
)
@click.option(
"--model_name_or_path", default=None, help="Name or the path of the model."
)
@click.option(
"--labels", default="Negative,Positive", help="Comma-separated list of labels."
)
@click.option(
"--title", default="ZenML NLP Use-Case", help="Title of the Gradio interface."
)
@click.option(
"--description",
default="Text Classification - Sentiment Analysis - ZenML - Gradio",
help="Description of the Gradio interface.",
)
@click.option(
"--interpretation",
default="default",
help="Interpretation mode for the Gradio interface.",
)
@click.option(
"--examples",
default="This is an awesome journey, I love it!",
help="Comma-separated list of examples to show in the Gradio interface.",
)
@click.option(
"--pipeline_version",
default="3",
help="Which version of the deploy pipeline should be deployed.",
type=int
)
def sentiment_analysis(
tokenizer_name_or_path: Optional[str],
model_name_or_path: Optional[str],
labels: Optional[str],
title: Optional[str],
description: Optional[str],
interpretation: Optional[str],
pipeline_version: int,
examples: Optional[str]
):
"""Launches a Gradio interface for sentiment analysis.
This function launches a Gradio interface for text-classification.
It loads a model and a tokenizer from the provided paths and uses
them to predict the sentiment of the input text.
Args:
tokenizer_name_or_path (str): Name or the path of the tokenizer.
model_name_or_path (str): Name or the path of the model.
labels (str): Comma-separated list of labels.
title (str): Title of the Gradio interface.
description (str): Description of the Gradio interface.
interpretation (str): Interpretation mode for the Gradio interface.
pipeline_version (int): Which pipeline version to user
examples (str): Comma-separated list of examples to show in the Gradio interface.
"""
labels = labels.split(",")
def preprocess(text: str) -> str:
"""Preprocesses the text.
Args:
text (str): Input text.
Returns:
str: Preprocessed text.
"""
new_text = []
for t in text.split(" "):
t = "@user" if t.startswith("@") and len(t) > 1 else t
t = "http" if t.startswith("http") else t
new_text.append(t)
return " ".join(new_text)
def softmax(x):
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(axis=0)
def analyze_text(inference_type, text):
if inference_type == "local":
cur_path = os.path.abspath(dirname(__file__))
model_path, tokenizer_path = cur_path, cur_path
if model_name_or_path:
model_path = f"{dirname(__file__)}/{model_name_or_path}/"
print(f"Loading model from {model_path}")
if tokenizer_name_or_path:
tokenizer_path = f"{dirname(__file__)}/{tokenizer_name_or_path}/"
print(f"Loading tokenizer from {tokenizer_path}")
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors="pt")
output = model(**encoded_input)
scores_ = output[0][0].detach().numpy()
scores_ = softmax(scores_)
scores = {l: float(s) for (l, s) in zip(labels, scores_)}
else:
client = Client()
latest_run = client.get_pipeline(
"sentinment_analysis_deploy_pipeline", version=pipeline_version
).runs[0]
endpoint_name = (
latest_run.steps["deploy_hf_to_sagemaker"]
.outputs["sagemaker_endpoint_name"]
.load()
)
predictor = sagemaker.Predictor(
endpoint_name=endpoint_name,
sagemaker_session=get_sagemaker_session(),
serializer=sagemaker.serializers.JSONSerializer(),
deserializer=sagemaker.deserializers.JSONDeserializer(),
)
res = predictor.predict({"inputs": text})
if res[0]["label"] == "LABEL_1":
scores = {"Negative": 1 - res[0]["score"], "Positive": res[0]["score"]}
else:
scores = {"Negative": res[0]["score"], "Positive": 1 - res[0]["score"]}
return scores
demo = gr.Interface(
fn=analyze_text,
inputs=[
gr.Dropdown(
["local", "sagemaker"], label="Select inference type", value="sagemaker"
),
gr.TextArea("Write your text or tweet here", label="Analyze Text"),
],
outputs=["label"],
title=title,
description=description,
interpretation=interpretation,
)
demo.launch(share=True, debug=True)
if __name__ == "__main__":
sentiment_analysis()
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