Create model.py

model.py

@@ -0,0 +1,24 @@
+from transformers import pipeline
+
+# You can switch to legal-specific model here
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+def classify_clauses(clauses):
+    results = []
+    for clause in clauses:
+        result = classifier(clause)
+        score = result[0]['score']
+        label = result[0]['label']
+        risk_score = score if label == 'POSITIVE' else 1 - score
+
+        risk_level = (
+            "High" if risk_score > 0.7 else
+            "Medium" if risk_score > 0.4 else
+            "Low"
+        )
+        results.append({
+            "clause": clause,
+            "risk_score": risk_score,
+            "risk_level": risk_level
+        })
+    return results