Update gradio_interface.py

gradio_interface.py

@@ -1,21 +1,55 @@
-# Import necessary libraries
 import gradio as gr
-import spacy
 
-# Load the trained spaCy model
-model_path = "./my_trained_model"
-nlp = spacy.load(model_path)
+# Function to execute evaluate_model.py
+def evaluate_model_script():
+    import spacy
+    import jsonlines
+    from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
 
-# Function to classify text
-def classify_text(text):
-    doc = nlp(text)
-    predicted_labels = doc.cats
-    return predicted_labels
+    # Load the trained spaCy model
+    nlp = spacy.load("./my_trained_model")
 
-# Gradio Interface
-def classify_and_save(input_text):
-    predicted_labels = classify_text(input_text)
-    return predicted_labels
+    # Load the golden evaluation data
+    golden_eval_data = []
+    with jsonlines.open("data/goldenEval.jsonl") as reader:
+        for record in reader:
+            golden_eval_data.append(record)
+
+    # Predict labels for each record using your model
+    predicted_labels = []
+    for record in golden_eval_data:
+        text = record["text"]
+        doc = nlp(text)
+        predicted_labels.append(doc.cats)
+
+    # Extract ground truth labels from the golden evaluation data
+    true_labels = [record["accept"] for record in golden_eval_data]
+
+    # Convert label format to match sklearn's classification report format
+    true_labels_flat = [label[0] if label else "reject" for label in true_labels]
+    predicted_labels_flat = [max(pred, key=pred.get) for pred in predicted_labels]
+
+    # Calculate evaluation metrics
+    accuracy = accuracy_score(true_labels_flat, predicted_labels_flat)
+    precision = precision_score(true_labels_flat, predicted_labels_flat, average='weighted')
+    recall = recall_score(true_labels_flat, predicted_labels_flat, average='weighted')
+    f1 = f1_score(true_labels_flat, predicted_labels_flat, average='weighted')
 
-iface = gr.Interface(classify_and_save, "textbox", "json", title="Text Classifier", description="Enter your text")
-iface.launch(share=True)
+    # Additional classification report
+    report = classification_report(true_labels_flat, predicted_labels_flat)
+
+    # Build the result dictionary
+    result = {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1_score": f1,
+        "detailed_classification_report": report
+    }
+
+    return result
+
+# Gradio Interface
+output = gr.outputs.Label(type="json", label="Evaluation Metrics")
+iface = gr.Interface(fn=evaluate_model_script, outputs=output, title="Evaluate Model Script")
+iface.launch()