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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)
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