Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load multi-class topic classification pipeline
+topic_pipeline = pipeline(
+    "text-classification",
+    model="AfroLogicInsect/topic-model-analysis-model",
+    tokenizer="AfroLogicInsect/topic-model-analysis-model",
+    return_all_scores=True
+)
+
+def predict_topics(text):
+    if not text.strip():
+        return [["Please enter some text", 0.0]]
+
+    results = topic_pipeline(text)
+    sorted_results = sorted(results[0], key=lambda x: x['score'], reverse=True)[:5]
+
+    # Format for Gradio output: list of [label, score]
+    return [[res['label'], round(res['score'], 3)] for res in sorted_results]
+
+iface = gr.Interface(
+    fn=predict_topics,
+    inputs=gr.Textbox(label="Enter text"),
+    outputs=gr.Dataframe(
+        headers=["Topic", "Confidence"],
+        label="Top 5 Predicted Topics",
+        type="array"
+    )
+)
+
+iface.launch(share=True)