Update app.py

app.py

@@ -13,124 +13,155 @@ def analyze(
     albumin, creatinine, glucose, crp, mcv, rdw, alp,
     wbc, lymph, age, gender, height, weight
 ):
-    # Calculate BMI (hidden from user, only passed to LLM)
+    # Calculate BMI
     try:
         height_m = height / 100  # cm → m
         bmi = round(weight / (height_m ** 2), 2)
     except Exception:
         bmi = "N/A"
 
-    # System-style instruction (cleaned: no estimates, no longevity scores)
-    system_prompt = (
-    """You are a professional AI Medical Assistant.
-You are analyzing patient demographics and Levine biomarker panel values.
-Output MUST strictly follow this structured format — no extra commentary, no estimates, no deviations:
+    # Improved system prompt with clearer instructions
+    system_prompt = """You are a professional AI Medical Assistant analyzing patient biomarkers.
+
+CRITICAL: Generate a COMPLETE report following this EXACT structure. Do not stop mid-sentence.
+
+=== REQUIRED OUTPUT FORMAT ===
 
 1. Executive Summary
-   - Top Priority Issues
-   - Key Strengths
+   - Top Priority Issues: [List 2-3 main concerns]
+   - Key Strengths: [List 2-3 positive findings]
 
 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
+   - Kidney Health (Creatinine)
+   - Metabolic Health (Glucose, CRP)
 
 3. Personalized Action Plan
-   - Medical (tests/consults)
-   - Nutrition (diet & supplements)
-   - Lifestyle (hydration, exercise, sleep)
-   - Testing (follow-up labs: ferritin, Vitamin D, GGT)
+   - Medical: [Recommended tests/consultations]
+   - Nutrition: [Dietary recommendations and supplements]
+   - Lifestyle: [Exercise, hydration, sleep guidance]
+   - Testing: [Follow-up labs needed]
 
 4. Interaction Alerts
-   - How biomarkers interact (e.g., anemia ↔ infection cycle, ALP with bone/liver origin)
+   [Explain how biomarkers interact and influence each other]
+
+6. Tabular Mapping
+   | Biomarker | Value | Status | AI Insight | Client Message |
+   |-----------|-------|--------|------------|----------------|
+   [Complete table for all biomarkers]
 
-6. Tabular Mapping (Biomarker → Value → Status → AI-Inferred Insight → Client-Friendly Message)
+7. Enhanced AI Insights
+   - Subclinical Nutrient Analysis (Iron, B12, Folate status)
+   - ALP Interpretation (bone vs liver origin)
+   - Immune System Assessment (WBC & lymphocyte trends)
+   - Long-term Health Considerations
 
-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
-"""
-)
+=== END FORMAT ===
 
-    # Construct profile input
+Now analyze the following patient data and provide a COMPLETE report:"""
+
+    # Construct patient profile
     patient_input = f"""
 Patient Profile:
-- Age: {age}
+- Age: {age} years
 - Gender: {gender}
 - Height: {height} cm
 - Weight: {weight} kg
 - BMI: {bmi}
 
-Lab Values:
+Laboratory 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
+- C-Reactive Protein (CRP): {crp} mg/L
+- Mean Cell Volume (MCV): {mcv} fL
+- Red Cell Distribution Width (RDW): {rdw} %
+- Alkaline Phosphatase (ALP): {alp} U/L
+- White Blood Cell Count (WBC): {wbc} K/uL
 - Lymphocyte Percentage: {lymph} %
-"""
-
-    prompt = system_prompt + "\n" + patient_input
-
-    # Call LLM
-    result = pipe(
-        prompt,
-        max_new_tokens=1000,
-        do_sample=True,
-        temperature=0.3,
-        top_p=0.9,
-        return_full_text=False
-    )
-
-    # 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:
-    gr.Markdown("## 🧪 Wellness Insights AI — Enter Profile Data")
 
-    with gr.Row():
-        albumin = gr.Number(label="Albumin (g/dL)")
-        wbc = gr.Number(label="White Blood Cell Count (K/uL)")
-
-    with gr.Row():
-        creatinine = gr.Number(label="Creatinine (mg/dL)")
-        lymph = gr.Number(label="Lymphocyte Percentage (%)")
+Generate complete analysis now:"""
 
-    with gr.Row():
-        glucose = gr.Number(label="Glucose (mg/dL)")
-        age = gr.Number(label="Age (years)")
-
-    with gr.Row():
-        crp = gr.Number(label="C-Reactive Protein (mg/L)")
-        gender = gr.Dropdown(choices=["Male", "Female"], label="Gender")
+    prompt = system_prompt + "\n" + patient_input
 
-    with gr.Row():
-        mcv = gr.Number(label="Mean Cell Volume (fL)")
-        height = gr.Number(label="Height (cm)")
+    try:
+        # Increased max_new_tokens significantly for complete output
+        result = pipe(
+            prompt,
+            max_new_tokens=2500,  # INCREASED from 1000
+            do_sample=True,
+            temperature=0.7,  # INCREASED from 0.3 for better generation
+            top_p=0.92,
+            top_k=50,
+            repetition_penalty=1.1,  # Prevent repetition
+            return_full_text=False,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+        output_text = result[0]["generated_text"].strip()
+        
+        # Clean up output - remove any prompt leakage
+        if "Executive Summary" in output_text:
+            idx = output_text.find("Executive Summary")
+            output_text = output_text[idx:]
+        elif "1. Executive Summary" in output_text:
+            idx = output_text.find("1. Executive Summary")
+            output_text = output_text[idx:]
+        
+        # If output seems incomplete, add a note
+        if len(output_text) < 500:
+            output_text += "\n\n⚠️ Note: Output may be incomplete. Consider re-running the analysis."
+        
+        return output_text
+    
+    except Exception as e:
+        return f"Error during analysis: {str(e)}\n\nPlease check your input values and try again."
+
+
+# Build Gradio UI with improved layout
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🧪 Wellness Insights AI
+    ### Enter Patient Profile & Lab Values for Comprehensive Analysis
+    """)
 
     with gr.Row():
-        rdw = gr.Number(label="Red Cell Distribution Width (%)")
-        weight = gr.Number(label="Weight (kg)")
+        with gr.Column():
+            gr.Markdown("### 👤 Demographics")
+            age = gr.Number(label="Age (years)", value=30)
+            gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
+            height = gr.Number(label="Height (cm)", value=175)
+            weight = gr.Number(label="Weight (kg)", value=70)
+        
+        with gr.Column():
+            gr.Markdown("### 🩸 Blood Health Markers")
+            wbc = gr.Number(label="White Blood Cell Count (K/uL)", value=7.0)
+            lymph = gr.Number(label="Lymphocyte Percentage (%)", value=30)
+            mcv = gr.Number(label="Mean Cell Volume (fL)", value=90)
+            rdw = gr.Number(label="Red Cell Distribution Width (%)", value=13)
 
     with gr.Row():
-        alp = gr.Number(label="Alkaline Phosphatase (U/L)")
-
-    analyze_btn = gr.Button("🔎 Analyze")
-    output = gr.Textbox(label="AI Lab Report (No Predictions)", lines=12)
+        with gr.Column():
+            gr.Markdown("### 🫀 Metabolic Markers")
+            glucose = gr.Number(label="Glucose (mg/dL)", value=95)
+            crp = gr.Number(label="C-Reactive Protein (mg/L)", value=1.5)
+        
+        with gr.Column():
+            gr.Markdown("### 🧬 Organ Function Markers")
+            albumin = gr.Number(label="Albumin (g/dL)", value=4.2)
+            creatinine = gr.Number(label="Creatinine (mg/dL)", value=1.0)
+            alp = gr.Number(label="Alkaline Phosphatase (U/L)", value=70)
+
+    analyze_btn = gr.Button("🔎 Generate Comprehensive Analysis", variant="primary", size="lg")
+    
+    gr.Markdown("### 📋 Analysis Report")
+    output = gr.Textbox(
+        label="AI-Generated Lab Report",
+        lines=25,
+        max_lines=50,
+        show_copy_button=True
+    )
 
     analyze_btn.click(
         fn=analyze,
@@ -138,5 +169,11 @@ with gr.Blocks() as demo:
                 wbc, lymph, age, gender, height, weight],
         outputs=output
     )
-
-demo.launch()
+    
+    gr.Markdown("""
+    ---
+    **Note:** This tool provides educational insights based on biomarker analysis. 
+    Always consult healthcare professionals for medical advice.
+    """)
+
+demo.launch(share=False)