app.py

import gradio as gr
import time
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_id = "juliensimon/xlm-v-base-language-id"

p = pipeline("text-classification", model=model_id)

ov_model = OVModelForSequenceClassification.from_pretrained(
    model_id, from_transformers=True
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

ov_p = pipeline("text-classification", model=ov_model, tokenizer=tokenizer)

# Warmum
for i in range(100):
    ov_p("Hello world")


def process(text, model, top_k=5):
    if model == "Vanilla":
        pipe = p
    else:
        pipe = ov_p
    tick = time.time()
    pred = pipe(text, top_k=top_k)
    tock = time.time()
    scores = {x["label"]: x["score"] for x in pred}
    msg = f"{(tock-tick)*1000:.2f} milliseconds"
    return scores, msg


# Gradio inputs
input_text = gr.Text(label="Enter text")
input_model = gr.Radio(label="Pick a model", choices=["Vanilla", "OpenVINO"])

# Gradio outputs
output_labels = gr.Label(label="Languages", num_top_classes=5)
output_text = gr.Text(label="Prediction time")

description = "This Space lets you perform language identification on the 102 languages present in the google/fleurs dataset. The underlying model scores 99.3% accuracy on the validation set. Inference is optimized with Optimum Intel and OpenVINO."

iface = gr.Interface(
    description=description,
    fn=process,
    inputs=[input_text, input_model],
    outputs=[output_labels, output_text],
    examples=[
        "Kila mtu ana haki ya kuelimishwa. Elimu yapasa itolewe bure hasa ile ya madarasa ya chini. Elimu ya masarasa ya chini ihudhuriwe kwa lazima. Elimu ya ufundi na ustadi iwe wazi kwa wote. Na elimu ya juu iwe wazi kwa wote kwa kutegemea sifa ya mtu",
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    ],
    allow_flagging="never",
)

iface.launch()