Update gradio_interface.py

gradio_interface.py

@@ -19,7 +19,7 @@ def classify_text(text):
 def evaluate_text(input_text):
     # Get the predicted labels and probabilities for the input text
     doc = nlp(input_text)
-    predicted_labels = {label: doc.cats[label] for label in doc.cats}
+    predicted_labels = 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.
@@ -32,17 +32,17 @@ def evaluate_text(input_text):
     }
 
     # 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]
+    predicted_labels_list = [predicted_labels[label] for label in ground_truth_labels]
+    ground_truth_labels_list = [ground_truth_labels[label] for label in ground_truth_labels]
 
     # 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')
+    accuracy = accuracy_score(ground_truth_labels_list, [1 if prob > threshold else 0 for prob in predicted_labels_list])
+    precision = precision_score(ground_truth_labels_list, [1 if prob > threshold else 0 for prob in predicted_labels_list], average='weighted')
+    recall = recall_score(ground_truth_labels_list, [1 if prob > threshold else 0 for prob in predicted_labels_list], average='weighted')
+    f1 = f1_score(ground_truth_labels_list, [1 if prob > threshold else 0 for prob in predicted_labels_list], average='weighted')
 
     # Additional classification report
-    report = classification_report(ground_truth_labels_list, predicted_labels_list)
+    report = classification_report(ground_truth_labels_list, [1 if prob > threshold else 0 for prob in predicted_labels_list])
 
     # Construct output dictionary
     output_dict = {
@@ -60,4 +60,4 @@ def evaluate_text(input_text):
 
 # Gradio Interface
 iface = gr.Interface(fn=evaluate_text, inputs="text", outputs="json", title="Text Evaluation-Manjinder", description="Enter your text")
-iface.launch(share=True)
+iface.launch(share=True)