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.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

README.md

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 ---
-title: Dbm
-emoji: 👀
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.49.1
+title: dbm
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import pandas as pd
+
+def predict_benefit(plan_type, drug_tier, member_age):
+    """
+    Simple demo logic: mock Drug Benefit Management score calculator.
+    Replace this with your model or logic.
+    """
+    base_cost = 100
+    tier_factor = {"Tier 1": 0.8, "Tier 2": 1.0, "Tier 3": 1.3}.get(drug_tier, 1.0)
+    plan_factor = {"Basic": 1.1, "Standard": 1.0, "Premium": 0.9}.get(plan_type, 1.0)
+    age_factor = 1.2 if member_age > 65 else 1.0
+
+    total_cost = base_cost * tier_factor * plan_factor * age_factor
+    benefit_score = round(100 - total_cost / 2, 2)
+
+    return f"Estimated Benefit Score: {benefit_score}"
+
+# Gradio UI
+with gr.Blocks(title="Drug Benefit Management") as demo:
+    gr.Markdown("## 💊 Drug Benefit Management Model")
+    plan_type = gr.Dropdown(["Basic", "Standard", "Premium"], label="Plan Type")
+    drug_tier = gr.Dropdown(["Tier 1", "Tier 2", "Tier 3"], label="Drug Tier")
+    member_age = gr.Slider(18, 90, 45, label="Member Age")
+
+    output = gr.Textbox(label="Predicted Benefit Score")
+    submit = gr.Button("Calculate Benefit")
+
+    submit.click(predict_benefit, [plan_type, drug_tier, member_age], output)
+
+demo.launch(share=True)

requirements.txt

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+gradio>=4.0
+pandas

runtime.txt

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+python-3.10