Create evaluation.py

services/evaluation.py

@@ -0,0 +1,24 @@
+import pandas as pd
+from sklearn.metrics import classification_report, accuracy_score
+
+def evaluate_model(predict_func):
+    try:
+        df = pd.read_csv("data/eval_dataset.csv")
+
+        texts = df["text"].astype(str).tolist()
+        y_true = df["label"].str.capitalize().tolist()
+
+        y_pred = predict_func(texts)
+
+        report = classification_report(y_true, y_pred, output_dict=True)
+        acc = accuracy_score(y_true, y_pred)
+
+        return {
+            "accuracy": round(acc, 3),
+            "report": report
+        }
+
+    except Exception as e:
+        return {
+            "error": str(e)
+        }