Spaces:
Sleeping
Sleeping
File size: 2,146 Bytes
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"""Gradio interface for Hugging Face inference."""
import gradio as gr
from huggingface_hub import InferenceClient
try:
import spaces
SPACES_AVAILABLE = True
except ImportError:
SPACES_AVAILABLE = False
from app.config import get_settings
settings = get_settings()
client = InferenceClient(model=settings.model_name, token=settings.api_token)
def _predict(text: str) -> str:
"""Run inference on the input text."""
if not text.strip():
return "Please enter some text."
task = settings.task
try:
if task in ("text-classification", "sentiment-analysis"):
results = client.text_classification(text)
output = "\n".join(
[f"{r['label']}: {r['score']:.2%}" for r in results]
)
elif task == "text-generation":
output = client.text_generation(text, max_new_tokens=100)
elif task == "summarization":
output = client.summarization(text)
elif task == "translation":
output = client.translation(text)
elif task == "fill-mask":
results = client.fill_mask(text)
output = "\n".join(
[f"{r['token_str']}: {r['score']:.2%}" for r in results]
)
else:
output = str(client.post(json={"inputs": text}))
return output
except Exception as e:
return f"Error: {e}"
# Apply @spaces.GPU decorator only on HF Spaces
if SPACES_AVAILABLE:
predict = spaces.GPU(duration=60)(_predict)
else:
predict = _predict
demo = gr.Interface(
fn=predict,
inputs=gr.Textbox(
label="Input Text",
placeholder="Enter text here...",
lines=4,
),
outputs=gr.Textbox(label="Result", lines=6),
title="Hugging Face Inference",
description=f"Model: **{settings.model_name}** | Task: **{settings.task}**",
examples=[
["I love this product! It's amazing."],
["This is the worst experience ever."],
["The weather is nice today."],
],
flagging_mode="never",
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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