Create app.py

app.py

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+import mlflow
+import mlflow.sklearn
+from sklearn.datasets import load_diabetes
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from pyngrok import ngrok
+import gradio as gr
+
+# MLflow setup
+mlflow.set_tracking_uri("./mlruns")  # Local directory for tracking
+mlflow.set_experiment("House Price Prediction")
+
+# Training function
+def train_and_log_model():
+    # Load dataset
+    data = load_diabetes()
+    X = data.data
+    y = data.target
+
+    # Split dataset
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train model
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    # Predict and evaluate
+    y_pred = model.predict(X_test)
+    mse = mean_squared_error(y_test, y_pred)
+
+    # Log to MLflow
+    with mlflow.start_run():
+        mlflow.log_param("model", "Linear Regression")
+        mlflow.log_metric("mse", mse)
+        mlflow.sklearn.log_model(model, "model")
+
+    return mse, "Model training complete and logged to MLflow!"
+
+# Start MLflow UI with Ngrok
+def start_mlflow_ui():
+    public_url = ngrok.connect(5000)  # Expose the MLflow UI running on port 5000
+    mlflow_command = "mlflow ui --host 0.0.0.0 --port 5000"
+    return_code = os.system(mlflow_command)
+    if return_code != 0:
+        return "Error: Unable to start MLflow UI."
+    return f"MLflow UI is accessible at {public_url}"
+
+# Gradio Interface Functions
+def train_model():
+    mse, message = train_and_log_model()
+    return f"MSE: {mse}\n{message}"
+
+def get_mlflow_ui_link():
+    public_url = start_mlflow_ui()
+    return public_url
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## House Price Prediction with MLflow")
+    train_btn = gr.Button("Train Model and Log to MLflow")
+    mlflow_btn = gr.Button("Start MLflow UI")
+    output = gr.Textbox(label="Output")
+    
+    train_btn.click(train_model, inputs=[], outputs=output)
+    mlflow_btn.click(get_mlflow_ui_link, inputs=[], outputs=output)
+
+# Launch Gradio App
+if __name__ == "__main__":
+    demo.launch()