Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# --------------------------------------------------
+# Configuration
+# --------------------------------------------------
+MODEL_NAME = "Abelex/Sentence-Chunking-Afri_BERTA_amharic_longtext"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# --------------------------------------------------
+# Load model and tokenizer
+# --------------------------------------------------
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.to(DEVICE)
+model.eval()
+
+# --------------------------------------------------
+# Prediction function
+# --------------------------------------------------
+def classify_text(text):
+    if text.strip() == "":
+        return "⚠️ Please enter Amharic text.", {}
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=512
+    ).to(DEVICE)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.softmax(logits, dim=-1)[0]
+
+    # Predicted label
+    pred_id = torch.argmax(probs).item()
+    pred_label = model.config.id2label.get(pred_id, str(pred_id))
+
+    # All label probabilities
+    scores = {
+        model.config.id2label.get(i, str(i)): float(probs[i])
+        for i in range(len(probs))
+    }
+
+    return pred_label, scores
+
+# --------------------------------------------------
+# Gradio UI
+# --------------------------------------------------
+with gr.Blocks(title="Amharic Text Classification") as demo:
+    gr.Markdown(
+        """
+        ## 📄 Amharic Text Classification  
+        This app classifies **Amharic long text** using a pretrained **AfriBERTa model**.
+        """
+    )
+
+    input_text = gr.Textbox(
+        lines=8,
+        placeholder="እባክዎ የአማርኛ ጽሑፍ እዚህ ያስገቡ...",
+        label="Input Text"
+    )
+
+    classify_btn = gr.Button("🔍 Classify")
+
+    output_label = gr.Label(label="Predicted Label")
+    output_scores = gr.JSON(label="Class Probabilities")
+
+    classify_btn.click(
+        fn=classify_text,
+        inputs=input_text,
+        outputs=[output_label, output_scores]
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **Model:** Abelex/Sentence-Chunking-Afri_BERTA_amharic_longtext  
+        Built with ❤️ using Gradio & Hugging Face
+        """
+    )
+
+# --------------------------------------------------
+# Launch app
+# --------------------------------------------------
+if __name__ == "__main__":
+    demo.launch()