File size: 1,958 Bytes
5987a9f e33ced9 7af97a7 e33ced9 5987a9f e33ced9 f9612f8 e33ced9 7af97a7 f9612f8 1ea80cb f9612f8 7af97a7 e33ced9 |
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 |
import os
import subprocess
def install_packages():
packages = [
"torch",
"transformers",
"huggingface-hub",
"gradio",
"accelerate",
"onnxruntime",
"onnxruntime-tools",
"optimum",
]
for package in packages:
result = subprocess.run(f'pip install {package}', shell=True)
if result.returncode != 0:
print(f"Failed to install {package}")
else:
print(f"Successfully installed {package}")
install_packages()
import gradio as gr
from huggingface_hub import login
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
model_id = "HassamAliCADI/SentimentOnx"
hf_token = os.environ.get("NLP")
if hf_token:
login(hf_token)
else:
print("NLP token not found.")
model = ORTModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Set top_k=3 to get the top 3 results
pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer, top_k=3)
def classify_text(text):
results = pipe(text)
output = ""
# Loop through up to 3 results
for i, result in enumerate(results[:3]):
# Each result should be a dictionary, so access it with ['label'] and ['score']
output += f"Label {i+1}: {result['label']}, Score: {result['score']:.4f}\n"
return output
gr.Interface(
fn=classify_text,
title="Sentiment Classifier",
description="Enter text to classify sentiment",
inputs=gr.Textbox(
label="Input Text",
placeholder="Type something here..."
),
outputs=gr.Textbox(
label="Classification Results"
),
examples=[
["I am deeply disappointed in your bad performance in last league match loss, and quite disappointed, sad because of it."],
["I am very happy with your excellent performance!"]
]
).launch()
|