Create app.py

app.py

@@ -0,0 +1,92 @@
+import gradio as gr
+import requests
+import os
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+API_URL = "https://router.huggingface.co/hf-inference/models/joeddav/xlm-roberta-large-xnli"
+
+
+def classify(payload):
+    try:
+        # Gradio JSON input bazen list içinde gelir
+        if isinstance(payload, list):
+            payload = payload[0]
+
+        invoice_text = payload.get("invoice_text", "")
+        categories = payload.get("categories", [])
+
+        if not invoice_text or not categories:
+            return "NO_INPUT"
+
+        # Metni kırp ama Türkçe karakterleri bozmadan
+        invoice_text = invoice_text.strip()[:3000]
+
+        candidate_labels = [c["cat_name"].strip() for c in categories]
+
+        if not candidate_labels:
+            return "NO_CATEGORIES"
+
+        response = requests.post(
+            API_URL,
+            headers={
+                "Authorization": f"Bearer {HF_TOKEN}",
+                "Content-Type": "application/json"
+            },
+            json={
+                "inputs": invoice_text,
+                "parameters": {
+                    "candidate_labels": candidate_labels,
+                    "multi_label": False
+                }
+            },
+            timeout=120
+        )
+
+        print("STATUS:", response.status_code)
+        print("RESPONSE:", response.text[:500])
+
+        if response.status_code != 200:
+            return f"HTTP_ERROR_{response.status_code}"
+
+        result = response.json()
+
+        # xlm-roberta ve deberta her ikisi de dict döner:
+        # {"sequence": "...", "labels": [...], "scores": [...]}
+        # Ama HF bazen list of dict döner, ikisini de handle ediyoruz.
+
+        if isinstance(result, list):
+            # list of {"label": ..., "score": ...} formatı
+            if not result:
+                return "NO_RESULT"
+            result.sort(key=lambda x: x.get("score", 0), reverse=True)
+            return result[0].get("label", "NO_LABEL")
+
+        elif isinstance(result, dict):
+            labels = result.get("labels", [])
+            scores = result.get("scores", [])
+
+            if not labels:
+                return "NO_LABEL"
+
+            # En yüksek skorlu kategoriyi direkt döndür
+            # (random veya penalty yok — model kararına güven)
+            best_index = scores.index(max(scores)) if scores else 0
+            return labels[best_index]
+
+        else:
+            return "UNEXPECTED_RESPONSE"
+
+    except requests.exceptions.Timeout:
+        return "TIMEOUT"
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"ERROR: {str(e)}"
+
+
+gr.Interface(
+    fn=classify,
+    inputs="json",
+    outputs="text"
+).launch(ssr_mode=False)