Update python_Code/evaluate_model.py

python_Code/evaluate_model.py

@@ -1,55 +1,46 @@
-import gradio as gr
-
-# Function to execute evaluate_model.py
-def evaluate_model_script():
-    import spacy
-    import jsonlines
-    from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
-
-    # Load the trained spaCy model
-    nlp = spacy.load("./my_trained_model")
-
-    # Load the golden evaluation data
-    golden_eval_data = []
-    with jsonlines.open("data/goldenEval.jsonl") as reader:
-        for record in reader:
-            golden_eval_data.append(record)
-
-    # Predict labels for each record using your model
-    predicted_labels = []
-    for record in golden_eval_data:
-        text = record["text"]
-        doc = nlp(text)
-        predicted_labels.append(doc.cats)
-
-    # Extract ground truth labels from the golden evaluation data
-    true_labels = [record["accept"] for record in golden_eval_data]
-
-    # Convert label format to match sklearn's classification report format
-    true_labels_flat = [label[0] if label else "reject" for label in true_labels]
-    predicted_labels_flat = [max(pred, key=pred.get) for pred in predicted_labels]
-
-    # Calculate evaluation metrics
-    accuracy = accuracy_score(true_labels_flat, predicted_labels_flat)
-    precision = precision_score(true_labels_flat, predicted_labels_flat, average='weighted')
-    recall = recall_score(true_labels_flat, predicted_labels_flat, average='weighted')
-    f1 = f1_score(true_labels_flat, predicted_labels_flat, average='weighted')
-
-    # Additional classification report
-    report = classification_report(true_labels_flat, predicted_labels_flat)
-
-    # Build the result dictionary
-    result = {
-        "accuracy": accuracy,
-        "precision": precision,
-        "recall": recall,
-        "f1_score": f1,
-        "detailed_classification_report": report
-    }
-
-    return result
-
-# Gradio Interface
-output = gr.outputs.Label(type="json", label="Evaluation Metrics")
-iface = gr.Interface(fn=evaluate_model_script, outputs=output, title="Evaluate Model Script")
-iface.launch()
+import spacy
+import jsonlines
+from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
+
+# Load the trained spaCy model
+nlp = spacy.load("./my_trained_model")
+
+# Load the golden evaluation data
+golden_eval_data = []
+with jsonlines.open("data/goldenEval.jsonl") as reader:
+    for record in reader:
+        golden_eval_data.append(record)
+
+# Predict labels for each record using your model
+predicted_labels = []
+for record in golden_eval_data:
+    text = record["text"]
+    doc = nlp(text)
+    predicted_labels.append(doc.cats)
+
+# Extract ground truth labels from the golden evaluation data
+true_labels = [record["accept"] for record in golden_eval_data]
+
+# Convert label format to match sklearn's classification report format
+true_labels_flat = [label[0] if label else "reject" for label in true_labels]
+predicted_labels_flat = [max(pred, key=pred.get) for pred in predicted_labels]
+
+# Calculate evaluation metrics
+accuracy = accuracy_score(true_labels_flat, predicted_labels_flat)
+precision = precision_score(true_labels_flat, predicted_labels_flat, average='weighted')
+recall = recall_score(true_labels_flat, predicted_labels_flat, average='weighted')
+f1 = f1_score(true_labels_flat, predicted_labels_flat, average='weighted')
+
+# Additional classification report
+report = classification_report(true_labels_flat, predicted_labels_flat)
+
+# Print or save the evaluation metrics
+print("Evaluation Metrics:")
+print(f"Accuracy: {accuracy}")
+print(f"Precision: {precision}")
+print(f"Recall: {recall}")
+print(f"F1-Score: {f1}")
+
+# Print or save the detailed classification report
+print("Detailed Classification Report:")
+print(report)