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.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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README.md

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 ---
-title: MLOp Demo
-emoji: 🌖
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 5.49.1
+title: MLOp_demo
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
+# Healthcare Fraud Detection – Model Monitoring Demo
+
+This Hugging Face Space demonstrates data drift monitoring for a healthcare fraud detection model using:
+
+- Gradio
+- Scikit-learn
+- Evidently AI
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Click the button to generate a drift report.

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.datasets import make_classification
+from sklearn.ensemble import RandomForestClassifier
+from evidently.report import Report
+from evidently.metrics import DataDriftTable
+
+
+def run_monitoring():
+    # Generate synthetic healthcare claims data
+    X, y = make_classification(
+        n_samples=2000,
+        n_features=5,
+        weights=[0.95, 0.05],  # fraud = rare
+        random_state=42
+    )
+
+    df = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(5)])
+    df["fraud_flag"] = y
+
+    # Train model on first 1500 samples
+    model = RandomForestClassifier()
+    model.fit(df.iloc[:1500, :-1], df.iloc[:1500, -1])
+
+    # Create drift in production batch
+    df_prod = df.iloc[1500:].copy()
+    df_prod["feature_0"] = df_prod["feature_0"] * 3  # drift injected
+
+    # Generate Evidently drift report
+    report = Report(metrics=[DataDriftTable()])
+    report.run(reference_data=df.iloc[:1500], current_data=df_prod)
+
+    # Save to HTML
+    report.save_html("monitoring_report.html")
+
+    return "monitoring_report.html"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Healthcare Fraud Detection Model Monitoring\nThis example shows data drift monitoring using Evidently.")
+    btn = gr.Button("Run Monitoring")
+    file_output = gr.File()
+
+    btn.click(fn=run_monitoring, outputs=file_output)
+
+demo.launch(share=True)

requirements.txt

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+gradio==4.29.0
+pandas
+scikit-learn
+evidently==0.4.25