Create app.py

app.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn.functional as F
+import gradio as gr
+from transformers import BigBirdConfig, AutoTokenizer
+from huggingface_hub import hf_hub_download
+
+from bigbird_anayasa_classifier import BigBirdClassifier
+
+
+REPO_ID = "FiratIsmailoglu/bigbird_anayasa_classifier"
+
+# Load tokenizer and config
+tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
+config = BigBirdConfig.from_pretrained(REPO_ID)
+
+# Build model
+model = BigBirdClassifier(config)
+
+# Download weights from HF
+weights_path = hf_hub_download(REPO_ID, filename="anayasa_bigbird_classifier_model.bin")
+state_dict = torch.load(weights_path, map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+
+def classify(text):
+    enc = tokenizer(
+        text,
+        truncation=True,
+        padding="max_length",
+        max_length=3072,
+        return_tensors="pt",
+    )
+
+    with torch.no_grad():
+        logits = model(enc["input_ids"], enc["attention_mask"])
+        probs = F.softmax(logits, dim=-1)[0].numpy()
+
+    labels = config.id2label
+    pred = int(probs.argmax())
+    pred_label = labels[pred]
+
+    probs_dict = {labels[i]: float(probs[i]) for i in range(len(probs))}
+
+    return f"Predicted: **{pred_label}**", probs_dict
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# BigBird Tabanlı Metin Sınıflandırma")
+    text_input = gr.Textbox(lines=10, label="Metni girin")
+    out_label = gr.Markdown()
+    out_probs = gr.Label()
+
+    btn = gr.Button("Sınıflandır")
+    btn.click(classify, text_input, [out_label, out_probs])
+
+demo.launch()