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| 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) | |
| } |