Create app.py

app.py

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+# File: app.py
+
+import gradio as gr
+import pandas as pd
+import io
+import zipfile
+import matplotlib.pyplot as plt
+import seaborn as sns
+from logic.care_gap_engine import evaluate_care_gaps
+from logic.financial_model import estimate_financial_recovery
+from logic.nlp_report import generate_patient_summary
+from logic.exporter import export_to_docx_and_zip
+
+CONFIG = {
+    "base_rate": 9500,
+    "care_gap_rules": {
+        "Breast Cancer Screening": {"gender": "F", "min_age": 50, "max_age": 74, "interval_days": 730},
+        "Colorectal Cancer Screening": {"min_age": 50, "max_age": 75, "interval_days": 365*10},
+        "Blood Pressure Control": {"bp_threshold": 140},
+        "Follow-Up Care": {},
+        "Readmission Risk": {}
+    },
+    "sdoh_modifiers": [
+        "Housing_Instability", "Food_Insecurity", "Transportation_Access", "Health_Literacy"
+    ]
+}
+
+def run_analysis(file, tone, gap_filter, custom_prompt):
+    df = pd.read_csv(file.name)
+    df.fillna("", inplace=True)
+
+    care_gaps = evaluate_care_gaps(df, CONFIG)
+    financials = estimate_financial_recovery(care_gaps, df, CONFIG['base_rate'], CONFIG['sdoh_modifiers'])
+    merged = care_gaps.merge(financials, on="patient_id")
+
+    if gap_filter:
+        merged = merged[merged['care_gaps'].apply(lambda gaps: gap_filter in gaps)]
+
+    summaries = {}
+    for _, row in merged.iterrows():
+        summaries[row['patient_id']] = generate_patient_summary(row, tone, custom_prompt)
+
+    zip_bytes, file_names = export_to_docx_and_zip(merged, summaries)
+
+    # Risk score graph
+    plt.figure(figsize=(8, 4))
+    plt.hist(merged['risk_score'], bins=10)
+    plt.title("Risk Score Distribution")
+    plt.xlabel("Risk Score")
+    plt.ylabel("Number of Patients")
+    plt.tight_layout()
+    plt.savefig("risk_score_plot.png")
+
+    # Gap frequency bar chart
+    gap_freq = {}
+    for gaps in merged['care_gaps']:
+        for g in gaps:
+            gap_freq[g] = gap_freq.get(g, 0) + 1
+    gap_df = pd.DataFrame(list(gap_freq.items()), columns=["Gap", "Count"])
+    plt.figure(figsize=(10, 5))
+    sns.barplot(x="Count", y="Gap", data=gap_df.sort_values(by="Count", ascending=False))
+    plt.title("Care Gap Frequency")
+    plt.tight_layout()
+    plt.savefig("care_gap_freq.png")
+
+    # Top 10 revenue opportunities
+    top10 = merged.sort_values(by="projected_gain", ascending=False).head(10)
+    top10_chart_path = "top10_revenue.png"
+    plt.figure(figsize=(10, 5))
+    sns.barplot(x="projected_gain", y="patient_id", data=top10)
+    plt.title("Top 10 Patients by Projected Gain")
+    plt.xlabel("Projected Revenue Recovery ($)")
+    plt.ylabel("Patient ID")
+    plt.tight_layout()
+    plt.savefig(top10_chart_path)
+
+    return merged, zip_bytes, "risk_score_plot.png", "care_gap_freq.png", top10_chart_path
+
+iface = gr.Interface(
+    fn=run_analysis,
+    inputs=[
+        gr.File(label="Upload Patient CSV"),
+        gr.Dropdown(choices=["clinical", "executive", "casual"], label="Report Tone", value="executive"),
+        gr.Textbox(label="Filter by Care Gap (e.g. 'Breast Cancer Screening')", value=""),
+        gr.Textbox(label="Custom Prompt Override (optional)", value="")
+    ],
+    outputs=[
+        gr.Dataframe(label="Care Gap & Financial Report"),
+        gr.File(label="Download All Reports as ZIP"),
+        gr.Image(label="Risk Score Distribution"),
+        gr.Image(label="Care Gap Frequency"),
+        gr.Image(label="Top 10 Revenue Opportunities")
+    ],
+    title="AI Medicare Advantage Analyzer",
+    description="Upload a patient dataset, evaluate CMS care gaps and SDOH risk, forecast financial recovery, and generate NLP summaries with LLMs."
+)
+
+if __name__ == "__main__":
+    iface.launch()