Update gradio_interface.py

gradio_interface.py

@@ -1,6 +1,5 @@
 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"
@@ -9,6 +8,15 @@ nlp = spacy.load(model_path)
 # Threshold for classification
 threshold = 0.21
 
+# Ground truth labels
+ground_truth_labels = {
+    "CapitalRequirements": 0,
+    "ConsumerProtection": 1,
+    "RiskManagement": 0,
+    "ReportingAndCompliance": 1,
+    "CorporateGovernance": 0
+}
+
 # Function to classify text
 def classify_text(text):
     doc = nlp(text)
@@ -21,7 +29,7 @@ def evaluate_text(input_text):
     doc = nlp(input_text)
     predicted_labels = doc.cats
     
-    # Construct output dictionary with predicted labels and probabilities
+    # Construct output dictionary
     output_dict = {
         "PredictedLabels": {label: score for label, score in predicted_labels.items() if score > threshold}
     }
@@ -31,6 +39,7 @@ def evaluate_text(input_text):
 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