Update app.py

app.py

@@ -1,7 +1,14 @@
+import time 
+
 import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 import gradio as gr
 
 from datasets import load_dataset
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
 
 LOGS_DATASET_URI = 'pgurazada1/machine-failure-mlops-demo-logs'
 
@@ -12,14 +19,54 @@ def get_data():
     Pull the data into a dataframe
     """
     data = load_dataset(LOGS_DATASET_URI)
-    sample_df = data['train'].to_pandas().sample(5)
+    sample_df = data['train'].to_pandas().sample(100)
 
     return sample_df
+
+def load_training_data():
+    dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
+    data_df = dataset.data
+    
+    target = 'Machine failure'
+    numeric_features = [
+        'Air temperature [K]',
+        'Process temperature [K]',
+        'Rotational speed [rpm]',
+        'Torque [Nm]',
+        'Tool wear [min]'
+    ]
+    
+    categorical_features = ['Type']
+
+    X = data_df[numeric_features + categorical_features]
+    y = data_df[target]
+
+    Xtrain, Xtest, ytrain, ytest = train_test_split(
+        X, y,
+        test_size=0.2,
+        random_state=42
+    )
+
+    return Xtrain, ytrain
+
+def check_model_drift():
+    sample_df = get_data()
+    p_pos_label_training_data = 0.03475
+    training_data_size = 8000
+    p_pos_label_sample_logs = sample_df.prediction.value_counts()
+    
+    return p_pos_label_sample_logs
+    
     
 with gr.Blocks() as demo:
-    gr.Markdown("Real-time Monitoring Dashboard")
+    gr.Markdown("# Real-time Monitoring Dashboard")
+
+    gr.Markdown("Snapshot of live data")
+    
     with gr.Row():
         with gr.Column():
-            gr.DataFrame(get_data)
+            gr.DataFrame(get_data, every=5)
+        with gr.Column():
+            gr.TextBox(f"Data refreshed at {time.time()}", every=5)
 
 demo.queue().launch()