Update gradio_interface.py

gradio_interface.py

@@ -21,42 +21,75 @@ def evaluate_text(input_text):
     doc = nlp(input_text)
     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.
-    ground_truth_labels = {
-        "CapitalRequirements": 0,
-        "ConsumerProtection": 1,
-        "RiskManagement": 0,
-        "ReportingAndCompliance": 1,
-        "CorporateGovernance": 0
+    # Construct output dictionary with predicted labels and probabilities
+    output_dict = {
+        "PredictedLabels": {label: score for label, score in predicted_labels.items() if score > threshold}
     }
-
-    # Convert predicted and ground truth labels to lists
-    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, [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, [1 if prob > threshold else 0 for prob in 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
 
 # Gradio Interface
 iface = gr.Interface(fn=evaluate_text, inputs="text", outputs="json", title="Text Evaluation-Manjinder", description="Enter your text")
 iface.launch(share=True)
+
+# import gradio as gr
+# import spacy
+# from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
+
+# # Load the trained spaCy model
+# model_path = "./my_trained_model"
+# nlp = spacy.load(model_path)
+
+# # Threshold for classification
+# threshold = 0.21
+
+# # Function to classify text
+# def classify_text(text):
+#     doc = nlp(text)
+#     predicted_labels = doc.cats
+#     return predicted_labels
+
+# # Function to evaluate the predicted labels for the input text
+# def evaluate_text(input_text):
+#     # Get the predicted labels and probabilities for the input text
+#     doc = nlp(input_text)
+#     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.
+#     ground_truth_labels = {
+#         "CapitalRequirements": 0,
+#         "ConsumerProtection": 1,
+#         "RiskManagement": 0,
+#         "ReportingAndCompliance": 1,
+#         "CorporateGovernance": 0
+#     }
+
+#     # Convert predicted and ground truth labels to lists
+#     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, [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, [1 if prob > threshold else 0 for prob in 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
+
+# # Gradio Interface
+# iface = gr.Interface(fn=evaluate_text, inputs="text", outputs="json", title="Text Evaluation-Manjinder", description="Enter your text")
+# iface.launch(share=True)