Create app.py

app.py

@@ -0,0 +1,67 @@
+# 🏷️ 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")