Update app.py

app.py

@@ -26,38 +26,8 @@ def analyze(
         "You are an advanced AI medical assistant. "
         "Analyze the patient’s biomarkers and demographics. "
         "Provide a structured assessment including: "
-         """ 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
+         """Executive Summary,System-Specific Analysis,Personalized Action Plan,Interaction Alerts,Longevity Metrics,Enhanced AI Insights & Longitudinal Risk
+       
 """    )
 
     # Construct patient profile input
@@ -86,7 +56,7 @@ def analyze(
     # Call LLM (exclude echoed input)
     result = pipe(
         prompt,
-        max_new_tokens=3000,
+        max_new_tokens=2000,
         do_sample=True,
         temperature=0.6,
         return_full_text=False