Create evaluate.py

evaluate.py

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+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+from sklearn.metrics import classification_report
+import tensorflow as tf
+import pandas as pd
+
+def get_classification_report():
+    try:
+        # Load test data
+        df = pd.read_csv("test.csv")
+        texts = df["text"].tolist()
+        true_labels = df["label"].tolist()
+
+        # Load tokenizer and model
+        tokenizer = AutoTokenizer.from_pretrained("Shrish/mbert-sentiment")
+        model = TFAutoModelForSequenceClassification.from_pretrained("Shrish/mbert-sentiment")
+
+        # Tokenize
+        inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="tf")
+        outputs = model(inputs)
+        preds = tf.math.argmax(outputs.logits, axis=1).numpy()
+
+        # Generate report
+        report = classification_report(true_labels, preds, target_names=["negative", "neutral", "positive"])
+        return report
+    except Exception as e:
+        return f"⚠️ Error occurred: {str(e)}"