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import gradio as gr
import spacy
# 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 and probabilities for the input text
doc = nlp(input_text)
predicted_labels = doc.cats
# Construct output dictionary with likelihood for each label
output_dict = {
"PredictedLabels": {label: score for label, score in predicted_labels.items()}
}
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)
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