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# 🏷️ Zero-Shot Text Classification | CPU-only HF Space
import gradio as gr
from transformers import pipeline
# Load the zero-shot pipeline once at startup
classifier = pipeline(
"zero-shot-classification",
model="facebook/bart-large-mnli",
device=-1 # CPU only
)
def zero_shot(text: str, labels: str, multi_label: bool):
if not text.strip() or not labels.strip():
return []
# parse comma-separated labels
candidate_list = [lbl.strip() for lbl in labels.split(",") if lbl.strip()]
res = classifier(text, candidate_list, multi_label=multi_label)
# build table of [label, score]
table = [
[label, round(score, 3)]
for label, score in zip(res["labels"], res["scores"])
]
return table
with gr.Blocks(title="🏷️ Zero-Shot Classifier") as demo:
gr.Markdown(
"# 🏷️ Zero-Shot Text Classification\n"
"Paste any text, list your candidate labels (comma-separated),\n"
"choose single- or multi-label mode, and see scores instantly."
)
with gr.Row():
text_in = gr.Textbox(
label="Input Text",
lines=4,
placeholder="e.g. The new conditioner left my hair incredibly soft!"
)
labels_in = gr.Textbox(
label="Candidate Labels",
lines=2,
placeholder="e.g. Positive, Negative, Question, Feedback"
)
multi_in = gr.Checkbox(
label="Multi-label classification",
info="Assign multiple labels if checked; otherwise picks the top label."
)
run_btn = gr.Button("Classify 🏷️", variant="primary")
result_df = gr.Dataframe(
headers=["Label", "Score"],
datatype=["str", "number"],
interactive=False,
wrap=True,
label="Prediction Scores"
)
run_btn.click(
zero_shot,
inputs=[text_in, labels_in, multi_in],
outputs=result_df
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0")
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