Update gradio_interface.py

gradio_interface.py

@@ -1,30 +1,35 @@
+import gradio as gr
 import spacy
 import jsonlines
 from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
 
-def evaluate_model(input_file_path, model_path):
-    # Load the trained spaCy model
-    nlp = spacy.load(model_path)
-
-    # Load the evaluation data
-    eval_data = []
-    with jsonlines.open(input_file_path) as reader:
+# 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 model based on user input
+def evaluate_model(input_text):
+    # Load the golden evaluation data
+    golden_eval_data = []
+    with jsonlines.open("data/goldenEval.jsonl") as reader:
         for record in reader:
-            eval_data.append(record)
+            golden_eval_data.append(record)
 
-    # Predict labels for each record using the model
-    predicted_labels = []
-    for record in eval_data:
-        text = record["text"]
-        doc = nlp(text)
-        predicted_labels.append(doc.cats)
+    # Predict labels for the input text using the model
+    predicted_labels = classify_text(input_text)
 
-    # Extract ground truth labels from the evaluation data
-    true_labels = [record["accept"] for record in eval_data]
+    # 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]
+    predicted_labels_flat = [max(predicted_labels, key=predicted_labels.get) for _ in range(len(true_labels))]
 
     # Calculate evaluation metrics
     accuracy = accuracy_score(true_labels_flat, predicted_labels_flat)
@@ -44,18 +49,10 @@ def evaluate_model(input_file_path, model_path):
         "classification_report": report
     }
 
-# Example usage
-input_file_path = "data/goldenEval.jsonl"
-model_path = "./my_trained_model"
-evaluation_results = evaluate_model(input_file_path, model_path)
-
-# Print or save the evaluation results
-print("Evaluation Metrics:")
-print(f"Accuracy: {evaluation_results['accuracy']}")
-print(f"Precision: {evaluation_results['precision']}")
-print(f"Recall: {evaluation_results['recall']}")
-print(f"F1-Score: {evaluation_results['f1']}")
-
-# Print or save the detailed classification report
-print("Detailed Classification Report:")
-print(evaluation_results['classification_report'])
+# Gradio Interface for text classification
+classification_interface = gr.Interface(fn=classify_text, inputs="textbox", outputs="json", title="Text Classifier", description="Enter your text")
+classification_interface.launch(share=True)
+
+# Gradio Interface for model evaluation
+evaluation_interface = gr.Interface(fn=evaluate_model, inputs="textbox", outputs="json", title="Model Evaluation", description="Enter text to evaluate the model")
+evaluation_interface.launch(share=True)