Upload gradio_interface.py

gradio_interface.py

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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 for the input text
+    doc = nlp(input_text)
+    predicted_labels = {label: score > threshold for label, score in doc.cats.items()}
+    
+    # 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] 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)