Delete gradio_interface.py

gradio_interface.py

@@ -1,59 +0,0 @@
-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)
-
-# 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 for the input text
-    predicted_labels = classify_text(input_text)
-    
-    # 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 = [1 if predicted_labels[label] > 0.5 else 0 for label in predicted_labels]
-    ground_truth_labels_list = [ground_truth_labels[label] for label in predicted_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')
-
-    # 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
-
-# Gradio Interface
-iface = gr.Interface(fn=evaluate_text, inputs="text", outputs="json", title="Text Evaluation-Manjinder", description="Enter your text")
-iface.launch(share=True)