Update gradio_interface.py

gradio_interface.py

@@ -1,29 +1,26 @@
-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
+import spacy
+import jsonlines
+from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
 
+def evaluate_model(input_file_path, model_path):
     # Load the trained spaCy model
-    nlp = spacy.load("./my_trained_model")
+    nlp = spacy.load(model_path)
 
-    # Load the golden evaluation data
-    golden_eval_data = []
-    with jsonlines.open("data/goldenEval.jsonl") as reader:
+    # Load the evaluation data
+    eval_data = []
+    with jsonlines.open(input_file_path) as reader:
         for record in reader:
-            golden_eval_data.append(record)
+            eval_data.append(record)
 
-    # Predict labels for each record using your model
+    # Predict labels for each record using the model
     predicted_labels = []
-    for record in golden_eval_data:
+    for record in 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]
+    # Extract ground truth labels from the evaluation data
+    true_labels = [record["accept"] for record in 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]
@@ -38,18 +35,27 @@ def evaluate_model_script():
     # Additional classification report
     report = classification_report(true_labels_flat, predicted_labels_flat)
 
-    # Build the result dictionary
-    result = {
+    # Return evaluation metrics and report
+    return {
         "accuracy": accuracy,
         "precision": precision,
         "recall": recall,
-        "f1_score": f1,
-        "detailed_classification_report": report
+        "f1": f1,
+        "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()
+# Example usage
+input_file_path = "data/goldenEval.jsonl"
+model_path = "./my_trained_model"
+evaluation_results = evaluate_model(input_file_path, model_path)
+
+# Print or save the evaluation results
+print("Evaluation Metrics:")
+print(f"Accuracy: {evaluation_results['accuracy']}")
+print(f"Precision: {evaluation_results['precision']}")
+print(f"Recall: {evaluation_results['recall']}")
+print(f"F1-Score: {evaluation_results['f1']}")
+
+# Print or save the detailed classification report
+print("Detailed Classification Report:")
+print(evaluation_results['classification_report'])