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.gradio/certificate.pem

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

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 ---
 title: PriorAuthorization
-emoji: 📚
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 5.49.1
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
+# 📝 Prior Authorization Helper
+
+A lightweight demo that evaluates a Prior Authorization (PA) request using rule-based logic.
+
+## Features
+- Takes Drug, Diagnosis, and Clinical Notes
+- Produces: Approved / Denied / Needs More Info / Manual Review
+- Displays a table and summary decision
+- 100% deployable on Hugging Face Gradio Spaces
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Deploy on Hugging Face
+1. Go to https://huggingface.co/new-space  
+2. Select **Gradio**  
+3. Upload:
+   - app.py  
+   - requirements.txt  
+   - runtime.txt  
+   - README.md  
+4. Wait for build → Your app launches automatically

app.py

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+import gradio as gr
+import pandas as pd
+import re
+
+def evaluate_pa(drug, diagnosis, notes):
+    text = (drug + " " + diagnosis + " " + notes).lower()
+
+    # Simple rule-based checks
+    approved_keywords = ["medically necessary", "failed therapy", "step therapy completed"]
+    denial_keywords = ["cosmetic", "experimental", "not indicated"]
+    needinfo_keywords = ["missing", "need labs", "need documentation"]
+
+    # Decision logic
+    if any(k in text for k in approved_keywords):
+        decision = "Approved"
+        reason = "Meets medical necessity criteria."
+    elif any(k in text for k in denial_keywords):
+        decision = "Denied"
+        reason = "Does not meet required clinical criteria."
+    elif any(k in text for k in needinfo_keywords):
+        decision = "Needs More Information"
+        reason = "Additional documentation is required."
+    else:
+        decision = "Needs Manual Review"
+        reason = "Criteria unclear based on provided information."
+
+    df = pd.DataFrame({
+        "Drug": [drug],
+        "Diagnosis": [diagnosis],
+        "Decision": [decision],
+        "Reason": [reason]
+    })
+
+    return df, f"### Decision: **{decision}**\nReason: {reason}"
+
+# --- Interface ---
+with gr.Blocks(title="Prior Authorization Decision Helper") as demo:
+    gr.Markdown("## 📝 Prior Authorization Helper")
+    gr.Markdown("Enter drug, diagnosis, and clinical notes to generate a mock PA decision.")
+
+    with gr.Row():
+        drug = gr.Textbox(label="Drug Name", placeholder="Example: Ozempic")
+        diagnosis = gr.Textbox(label="Diagnosis", placeholder="Example: Type 2 Diabetes")
+
+    notes = gr.Textbox(
+        label="Clinical Notes",
+        placeholder="Example: Patient completed step therapy and met medically necessary criteria.",
+        lines=6
+    )
+
+    submit = gr.Button("Evaluate PA Request")
+
+    output_table = gr.Dataframe(label="PA Evaluation Summary")
+    output_text = gr.Markdown(label="Decision Result")
+
+    submit.click(evaluate_pa, [drug, diagnosis, notes], [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