Update Gradio app to integrate model and dataset

app.py

@@ -1,7 +1,31 @@
-import gradio as gr 
- 
-def greet(name): 
-    return "Hello " + name + "!!" 
- 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text") 
-demo.launch() 
+import gradio as gr
+from joblib import load
+
+# Load the model and preprocessing artifacts
+model = load("logistic_model.joblib")
+tfidf_vectorizer = load("tfidf_vectorizer.joblib")
+mlb = load("label_binarizer.joblib")
+
+# Define a function to classify commit messages
+def classify_commit(message):
+    # Preprocess the input message
+    X_tfidf = tfidf_vectorizer.transform([message])
+
+    # Predict the labels
+    prediction = model.predict(X_tfidf)
+    predicted_labels = mlb.inverse_transform(prediction)
+
+    # Return the predicted labels as a comma-separated string
+    return ", ".join(predicted_labels[0]) if predicted_labels[0] else "No labels"
+
+# Create a Gradio interface
+demo = gr.Interface(
+    fn=classify_commit,  # Function to call
+    inputs=gr.Textbox(label="Enter Commit Message"),  # Input: Textbox for commit message
+    outputs=gr.Textbox(label="Predicted Labels"),  # Output: Textbox for predicted labels
+    title="Commit Message Classifier",
+    description="Enter a commit message to classify it into predefined categories."
+)
+
+# Launch the Gradio app
+demo.launch()