Update gradio_interface.py

gradio_interface.py

@@ -17,9 +17,9 @@ def classify_text(text):
 
 # Function to evaluate the predicted labels for the input text
 def evaluate_text(input_text):
-    # Get the predicted labels for the input text
+    # Get the predicted labels and probabilities for the input text
     doc = nlp(input_text)
-    predicted_labels = {label: score > threshold for label, score in doc.cats.items()}
+    predicted_labels = {label: doc.cats[label] for label in doc.cats}
     
     # Assuming you have ground truth labels for the input text, you would compare the predicted labels with the ground truth labels here.
     # For demonstration purposes, let's assume the ground truth labels are provided here.
@@ -31,6 +31,33 @@ def evaluate_text(input_text):
         "CorporateGovernance": 0
     }
 
+    # Convert predicted and ground truth labels to lists
+    predicted_labels_list = [doc.cats[label] for label in doc.cats]
+    ground_truth_labels_list = [ground_truth_labels[label] for label in doc.cats]
+
+    # Calculate evaluation metrics
+    accuracy = accuracy_score(ground_truth_labels_list, predicted_labels_list)
+    precision = precision_score(ground_truth_labels_list, predicted_labels_list, average='weighted')
+    recall = recall_score(ground_truth_labels_list, predicted_labels_list, average='weighted')
+    f1 = f1_score(ground_truth_labels_list, predicted_labels_list, average='weighted')
+
+    # Additional classification report
+    report = classification_report(ground_truth_labels_list, predicted_labels_list)
+
+    # Construct output dictionary
+    output_dict = {
+        "PredictedLabels": predicted_labels,
+        "EvaluationMetrics": {
+            "Accuracy": accuracy,
+            "Precision": precision,
+            "Recall": recall,
+            "F1-Score": f1,
+            "ClassificationReport": report
+        }
+    }
+    
+    return output_dict
+
     # Convert predicted and ground truth labels to lists
     predicted_labels_list = [1 if predicted_labels[label] else 0 for label in predicted_labels]
     ground_truth_labels_list = [ground_truth_labels[label] for label in predicted_labels]