Update app.py

app.py

@@ -8,7 +8,6 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto")
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-
 # Function to build structured input and query the LLM
 def analyze(
     albumin, creatinine, glucose, crp, mcv, rdw, alp,
@@ -21,9 +20,14 @@ def analyze(
     except Exception:
         bmi = "N/A"
 
-    # System-style instruction
+    # System-style instruction (non-medical)
     system_prompt = (
-         """Executive Summary
+        """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:
+
+Executive Summary
    - Longevity Vitality Score (out of 100)
    - Top Priority Areas for Optimization
    - Key Strengths
@@ -52,46 +56,45 @@ def analyze(
    - Predicted nutrient needs (iron, B12, folate, copper) framed as optimization
    - Patterns in WBC/lymphocyte values for general resilience
    - Predictive wellness trends (non-medical)
+"""    
+    )
 
-"""    )
-
-    # Construct patient profile input
+    # Construct profile input
     patient_input = f"""
-    Patient Profile:
-    - Age: {age}
-    - Gender: {gender}
-    - Height: {height} cm
-    - Weight: {weight} kg
-    - BMI: {bmi}
-    
-    Lab Values:
-    - Albumin: {albumin} g/dL
-    - Creatinine: {creatinine} mg/dL
-    - Glucose: {glucose} mg/dL
-    - C-Reactive Protein: {crp} mg/L
-    - Mean Cell Volume: {mcv} fL
-    - Red Cell Distribution Width: {rdw} %
-    - Alkaline Phosphatase: {alp} U/L
-    - White Blood Cell Count: {wbc} K/uL
-    - Lymphocyte Percentage: {lymph} %
-    """
+Patient Profile:
+- Age: {age}
+- Gender: {gender}
+- Height: {height} cm
+- Weight: {weight} kg
+- BMI: {bmi}
+
+Lab Values:
+- Albumin: {albumin} g/dL
+- Creatinine: {creatinine} mg/dL
+- Glucose: {glucose} mg/dL
+- C-Reactive Protein: {crp} mg/L
+- Mean Cell Volume: {mcv} fL
+- Red Cell Distribution Width: {rdw} %
+- Alkaline Phosphatase: {alp} U/L
+- White Blood Cell Count: {wbc} K/uL
+- Lymphocyte Percentage: {lymph} %
+"""
 
     prompt = system_prompt + "\n" + patient_input
 
-    # Call LLM (exclude echoed input)
+    # Call LLM
     result = pipe(
         prompt,
-        max_new_tokens=2000,
+        max_new_tokens=1200,
         do_sample=True,
         temperature=0.6,
         return_full_text=False
     )
     return result[0]["generated_text"].strip()
 
-
 # Build Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧪 Medical Insights AI — Enter Patient Data")
+    gr.Markdown("## 🧪 Wellness Insights AI — Enter Profile Data")
 
     with gr.Row():
         albumin = gr.Number(label="Albumin (g/dL)")
@@ -121,9 +124,8 @@ with gr.Blocks() as demo:
         alp = gr.Number(label="Alkaline Phosphatase (U/L)")
 
     analyze_btn = gr.Button("🔎 Analyze")
-    output = gr.Textbox(label="AI Medical Assessment", lines=12)
+    output = gr.Textbox(label="AI Wellness Assessment", lines=12)
 
-    # Run analysis
     analyze_btn.click(
         fn=analyze,
         inputs=[albumin, creatinine, glucose, crp, mcv, rdw, alp,