Update app.py

app.py

@@ -20,43 +20,39 @@ def analyze(
     except Exception:
         bmi = "N/A"
 
-    # System-style instruction (non-medical)
+    # System-style instruction (non-medical, fixed headings)
     system_prompt = (
         """You are a wellness assistant. 
 Do NOT provide medical insights, diagnoses, or test recommendations. 
-Only use neutral, non-medical, wellness-oriented language (e.g., energy, balance, recovery, nutrition).
-Strictly follow the structure below and generate only these sections with no extra headings:
+Only use neutral, non-medical, wellness-oriented language 
+(e.g., energy, balance, recovery, nutrition).
+
+Strictly follow this structure, and start directly with "Executive Summary":
 
 Executive Summary
    - Longevity Vitality Score (out of 100)
    - Top Priority Areas for Optimization
    - Key Strengths
-
 2. System-Specific Overview
    - Blood & Energy Balance (MCV, RDW, WBC, Lymphocytes)
    - Protein & Recovery (Albumin, ALP)
    - Hydration & Recovery (Creatinine)
    - Energy Metabolism (Glucose, lnCRP)
-   - Nutrient & Wellness Factors (Vitamin D, Iron, Copper, B12)
-
 3. Personalized Action Plan (Non-Medical)
    - General Recommendations (hydration, exercise, sleep, nutrition tracking)
    - Nutrient Balance (dietary diversity, protein adequacy, micronutrient-rich foods)
    - Lifestyle Optimization (stress management, movement, sleep patterns)
    - Monitoring (re-check certain markers over time for trends)
-
 4. Biomarker Interactions
    - How values relate to energy, recovery, and resilience (non-medical framing)
-
 5. Longevity Metrics
    - Vitality Performance Score
    - Energy, Recovery & Cognitive Trajectory
-
 6. Enhanced AI Insights & Longitudinal Trends
    - Predicted nutrient needs (iron, B12, folate, copper) framed as optimization
    - Patterns in WBC/lymphocyte values for general resilience
    - Predictive wellness trends (non-medical)
-"""    
+"""
     )
 
     # Construct profile input
@@ -85,12 +81,19 @@ Lab Values:
     # Call LLM
     result = pipe(
         prompt,
-        max_new_tokens=1200,
+        max_new_tokens=3000,
         do_sample=True,
         temperature=0.6,
         return_full_text=False
     )
-    return result[0]["generated_text"].strip()
+
+    # Force output to start from "Executive Summary"
+    output_text = result[0]["generated_text"].strip()
+    if "Executive Summary" in output_text:
+        output_text = output_text.split("Executive Summary", 1)[-1]
+        output_text = "Executive Summary" + output_text
+
+    return output_text
 
 # Build Gradio UI
 with gr.Blocks() as demo: