Update app.py

app.py

@@ -25,48 +25,11 @@ def analyze(
         "You are an advanced AI medical assistant. "
         "Analyze the patient’s biomarkers and demographics. "
         "Provide a structured assessment including: "
-        "patient_profile, lab_results, risk_assessment, clinical_impression, recommendations. "
+        "patient_profile, lab_results, risk_assessment, clinical_impression,, recommendations, longitivity metrics and their executive summary "
     )
 
     # Construct patient profile input
     patient_input = f"""
-    You are a professional AI Medical Assistant.
-    You are analyzing patient demographics and *Levine biomarker panel* values.
-    Output MUST strictly follow this structured format:
-
-    1. Executive Summary
-       - Longevity Vitality Score (out of 100)
-       - Top Priority Issues
-       - Key Strengths
-
-    2. System-Specific Analysis
-       - Blood Health (MCV, RDW, Lymphocytes, WBC)
-       - Protein & Liver Health (Albumin, ALP)
-       - Kidney Health (Creatinine µmol/L)
-       - Metabolic Health (Glucose mmol/L, lnCRP)
-       - Other relevant systems
-
-        3. Personalized Action Plan
-       - Medical (tests/consults)
-       - Nutrition (diet & supplements)
-       - Lifestyle (hydration, exercise, sleep)
-       - Testing (follow-up labs: ferritin, Vitamin D, GGT)
-
-    4. Interaction Alerts
-       - How biomarkers interact (e.g., anemia ↔ infection cycle, ALP with bone/liver origin)
-
-    5. Longevity Metrics
-       - Metabolic Health Score
-       - Cardiovascular & Cognitive risk trajectory
-
-    6. Tabular Mapping (Biomarker → Value → Status → AI-Inferred Insight → Client-Friendly Message)
-
-    7. Enhanced AI Insights & Longitudinal Risk
-       - Subclinical nutrient predictions (Iron, B12, Folate, Copper)
-       - Elevated ALP interpretation (bone vs liver origin)
-       - WBC & lymphocyte trends for immunity
-       - Predictive longevity risk profile
-
     Patient Profile:
     - Age: {age}
     - Gender: {gender}