Update gradio_interface.py

gradio_interface.py

@@ -5,18 +5,6 @@ import spacy
 model_path = "./my_trained_model"
 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)
@@ -31,20 +19,16 @@ def evaluate_text(input_text):
     
     # Construct output dictionary with likelihood for each label
     output_dict = {
-        "PredictedLabels": {label: score for label, score in predicted_labels.items() if score > threshold}
+        "PredictedLabels": {label: score for label, score in predicted_labels.items()}
     }
     
-    # Add likelihood for each ground truth label
-    for label in ground_truth_labels:
-        if label not in output_dict["PredictedLabels"]:
-            output_dict["PredictedLabels"][label] = 0  # Set likelihood to 0 if label not present
-    
     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