Update app.py

app.py

@@ -186,3 +186,29 @@ def test_model():
         return {"sample_input": sample_text, "predictions": response}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/train")
+def train_model():
+    try:
+        df = pd.read_csv(DATA_PATH).fillna("")
+        df["text_input"] = df.apply(create_text_input, axis=1)
+        X = df["text_input"]
+        y = df[["Maker_Action", "Escalation_Level", "Risk_Category",
+                "Risk_Drivers", "Investigation_Outcome", "Red_Flag_Reason"]]
+
+        X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                            test_size=0.2,
+                                                            random_state=42)
+        pipeline = Pipeline([
+            ("vectorizer", TfidfVectorizer()),
+            ("classifier", MultiOutputClassifier(LogisticRegression(max_iter=1000)))
+        ])
+        pipeline.fit(X_train, y_train)
+        os.makedirs(MODEL_DIR, exist_ok=True)
+        joblib.dump(pipeline, MODEL_PATH)
+        accuracy = pipeline.score(X_test, y_test)
+        return {"message": "Model trained successfully", "accuracy": accuracy}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+