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.gradio/certificate.pem

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

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 ---
-title: Healthcare Fraud
-emoji: 📉
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 5.49.1
+title: Healthcare_Fraud
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
+# 🏥 Healthcare Fraud Detection – Demo App
+
+This app demonstrates a simple, rule-based fraud detection workflow commonly used in healthcare data science initiatives.
+
+### Fraud Patterns Included
+- Upcoding
+- Phantom Billing
+- Unbundling
+- High-dollar claim flags
+
+### Features
+- Accepts provider, procedure, amount, and claim notes
+- Produces a fraud score (High / Moderate / Low / None)
+- Displays detected fraud-related indicators
+- Lightweight and deployable on Hugging Face Spaces
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### Deploy
+1. Go to https://huggingface.co/new-space
+2. Hugging Face SDK: **Gradio**
+3. Upload these files:
+   - `app.py`
+   - `requirements.txt`
+   - `runtime.txt`
+   - `README.md`
+4. Build → App launches automatically

app.py

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+import gradio as gr
+import pandas as pd
+import re
+
+def detect_fraud(provider, procedure, amount, notes):
+    """
+    Lightweight rule-based fraud indicator for demonstration.
+    """
+
+    text = f"{provider} {procedure} {notes}".lower()
+
+    # Rule-based flags (common healthcare fraud patterns)
+    upcoding_terms = ["complex visit", "level 5", "high severity", "multiple procedures"]
+    phantom_terms = ["no patient", "patient denies", "never seen"]
+    unbundling_terms = ["billed separately", "duplicate", "split billing"]
+    high_amount_threshold = 5000  # simple flag
+
+    flags = []
+
+    # Fraud pattern detection
+    if any(term in text for term in upcoding_terms):
+        flags.append("Upcoding")
+
+    if any(term in text for term in phantom_terms):
+        flags.append("Phantom Billing")
+
+    if any(term in text for term in unbundling_terms):
+        flags.append("Unbundling")
+
+    if amount > high_amount_threshold:
+        flags.append("High-Dollar Flag")
+
+    # Score logic
+    if len(flags) >= 3:
+        score = "High Fraud Risk"
+    elif len(flags) == 2:
+        score = "Moderate Fraud Risk"
+    elif len(flags) == 1:
+        score = "Low Fraud Risk"
+    else:
+        score = "No Fraud Indicators Found"
+
+    df = pd.DataFrame({
+        "Provider": [provider],
+        "Procedure": [procedure],
+        "Amount": [amount],
+        "Detected Flags": [", ".join(flags) if flags else "None"],
+        "Fraud Risk Score": [score]
+    })
+
+    return df, f"### Fraud Risk: **{score}**\nFlags: {', '.join(flags) if flags else 'None'}"
+
+# ---- Gradio App UI ----
+
+with gr.Blocks(title="Healthcare Fraud Detection Demo") as demo:
+    gr.Markdown("## 🏥 Healthcare Fraud Detection – Demo (Rule-Based)")
+    gr.Markdown("Upload simple claim information to evaluate possible fraud indicators.")
+
+    with gr.Row():
+        provider = gr.Textbox(label="Provider Name", placeholder="Example: ABC Clinic")
+        procedure = gr.Textbox(label="Procedure Code/Description", placeholder="Example: Level 5 visit")
+
+    amount = gr.Number(label="Claim Amount ($)", value=1200)
+
+    notes = gr.Textbox(
+        label="Claim Notes",
+        lines=6,
+        placeholder="Example: Patient denies visit. Procedure billed separately..."
+    )
+
+    submit = gr.Button("Analyze Claim")
+
+    output_table = gr.Dataframe(label="Fraud Detection Summary")
+    output_text = gr.Markdown(label="Fraud Score")
+
+    submit.click(
+        detect_fraud,
+        inputs=[provider, procedure, amount, notes],
+        outputs=[output_table, output_text]
+    )
+
+demo.launch(share=True)

requirements.txt

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+gradio>=4.0
+pandas

runtime.txt

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+python-3.10