Update python_Code/evaluate_model.py

python_Code/evaluate_model.py

@@ -1,46 +1,55 @@
-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)
+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()