Initial

README.md

@@ -5,6 +5,7 @@ colorFrom: yellow
 colorTo: green
 sdk: gradio
 sdk_version: 5.45.0
+python_version: 3.12.11
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import pipeline
+
+pipe = pipeline("text-classification", model="kitrofimov/news-classifier", top_k=3)
+
+def classify(text):
+    preds = pipe(text)[0]
+    return {p["label"]: float(p["score"]) for p in preds}
+
+with gr.Blocks() as demo:
+    gr.Markdown("# News Classifier")
+    gr.Markdown("Paste a news article below and see which category it belongs to! (one of \"world\", \"sports\", \"business\" and \"science / technology\"")
+    gr.Markdown("This model is based on a [`distilbert-base-uncased`](https://huggingface.co/distilbert/distilbert-base-uncased) architecture and was fine-tuned on the [AG News](https://huggingface.co/datasets/fancyzhx/ag_news) dataset for 3 epochs. Training code [here](https://colab.research.google.com/drive/1KTai0S1dzwIoS3Sba_jJG9ZNISRjSKGo)")
+    
+    with gr.Row():
+        with gr.Column():
+            input = gr.Textbox(lines=5, placeholder="Enter your news article...")
+            classify_btn = gr.Button("Classify")
+        with gr.Column():
+            output = gr.Label(num_top_classes=3)
+
+    classify_btn.click(classify, inputs=input, outputs=output)
+
+demo.launch()
+
+

requirements.txt

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+transformers==4.56.1