Update app.py

app.py

@@ -12,9 +12,7 @@ from reportlab.platypus import TableStyle
 # Load model
 model = joblib.load("typhoidguard_model_reduced.pkl")
 
-# Risk & Agent Logic
 # Risk & Agentic Logic
-
 def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, potassium):
 
     # 1️⃣ Prepare data with exact column names
@@ -33,13 +31,18 @@ def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, po
 
     # 3️⃣ Model probability
     prob = model.named_steps["classifier"].predict_proba(X_processed)[0][1]
-    prob_pct = round(prob * 100, 2)
 
-    # 4️⃣ Clinical thresholds (override)
+    # 4️⃣ Clinical thresholds
     low_platelets = platelets < 150000
     low_hb = hb < 10
     prolonged_treatment = duration > 7
 
+    # 5️⃣ Adjust probability for extreme lab values
+    if low_platelets or low_hb:
+        prob_pct = 95.0  # clinically override
+    else:
+        prob_pct = round(prob * 100, 2)
+
     # Default risk label based on probability
     if prob > 0.65:
         risk_label = "🔴 HIGH RISK"
@@ -61,7 +64,7 @@ def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, po
     else:
         recommendation = "Continue standard treatment protocol."
 
-    # 5️⃣ Risk factors list
+    # 6️⃣ Risk factors list
     factors = []
     if low_platelets:
         factors.append("Low Platelet Count")
@@ -74,7 +77,7 @@ def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, po
     if potassium < 3.5 or potassium > 5.0:
         factors.append("Abnormal Potassium")
 
-    # 6️⃣ Construct HTML
+    # 7️⃣ Construct HTML
     explanation = f"""
     <div style="font-family:Arial; padding:15px;">
         <h2 style="color:{color};">Patient: {name}</h2>
@@ -90,43 +93,125 @@ def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, po
     """
 
     return explanation
+
+
 # PDF Generation
+# PDF Generation (with color coding & disclaimer)
 def generate_pdf(name, age, gender, platelets, hb, duration, calcium, potassium):
 
+    # 1️⃣ Prepare data for model & agentic logic
+    data = pd.DataFrame([{
+        "Platelet Count": platelets,
+        "Age": age,
+        "Hemoglobin (g/dL)": hb,
+        "Treatment Duration": duration,
+        "Calcium (mg/dL)": calcium,
+        "Potassium (mmol/L)": potassium,
+        "Gender": gender
+    }])
+
+    X_processed = model.named_steps["preprocessor"].transform(data)
+    prob = model.named_steps["classifier"].predict_proba(X_processed)[0][1]
+
+    low_platelets = platelets < 150000
+    low_hb = hb < 10
+    prolonged_treatment = duration > 7
+
+    # Adjust probability for critical lab values
+    if low_platelets or low_hb:
+        prob_pct = 95.0
+    else:
+        prob_pct = round(prob * 100, 2)
+
+    # Determine status and recommendation
+    if prob > 0.65:
+        risk_label = "HIGH RISK"
+        color = colors.red
+    elif prob >= 0.35:
+        risk_label = "MODERATE RISK"
+        color = colors.orange
+    else:
+        risk_label = "LOW RISK"
+        color = colors.green
+
+    if low_platelets or low_hb:
+        risk_label = "HIGH RISK"
+        color = colors.red
+        recommendation = "Immediate referral to secondary care facility recommended. Close monitoring required."
+    elif prob >= 0.35:
+        recommendation = "Close monitoring advised. Repeat blood tests within 48 hours."
+    else:
+        recommendation = "Continue standard treatment protocol."
+
+    # Clinical risk factors
+    factors = []
+    if low_platelets:
+        factors.append("Low Platelet Count")
+    if low_hb:
+        factors.append("Low Hemoglobin")
+    if prolonged_treatment:
+        factors.append("Prolonged Treatment Duration")
+    if calcium < 8.5:
+        factors.append("Low Calcium")
+    if potassium < 3.5 or potassium > 5.0:
+        factors.append("Abnormal Potassium")
+
+    # 2️⃣ Generate PDF
     file_path = f"{name}_Typhoid_Report.pdf"
     doc = SimpleDocTemplate(file_path)
     elements = []
     styles = getSampleStyleSheet()
 
+    # Title
     elements.append(Paragraph("<b>TyphoidGuard AI Clinical Report</b>", styles["Title"]))
-    elements.append(Spacer(1, 0.5 * inch))
+    elements.append(Spacer(1, 0.3 * inch))
 
-    data = [
+    # Patient Info Table
+    patient_data = [
         ["Patient Name", name],
         ["Age", age],
         ["Gender", gender],
         ["Platelet Count", platelets],
-        ["Hemoglobin", hb],
-        ["Treatment Duration", duration],
-        ["Calcium", calcium],
-        ["Potassium", potassium],
+        ["Hemoglobin (g/dL)", hb],
+        ["Treatment Duration (Days)", duration],
+        ["Calcium (mg/dL)", calcium],
+        ["Potassium (mmol/L)", potassium],
+        ["Risk of Treatment Failure (%)", prob_pct],
+        ["Status", risk_label],
     ]
 
-    table = Table(data)
+    table = Table(patient_data, colWidths=[200, 250])
     table.setStyle(TableStyle([
         ('BACKGROUND', (0,0), (-1,0), colors.lightblue),
-        ('GRID', (0,0), (-1,-1), 1, colors.grey)
+        ('GRID', (0,0), (-1,-1), 1, colors.grey),
+        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
+        ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
+        # Color coding for risk status
+        ('TEXTCOLOR', (-1,-1), (-1,-1), color)
     ]))
-
     elements.append(table)
-    elements.append(Spacer(1, 0.5 * inch))
+    elements.append(Spacer(1, 0.3 * inch))
+
+    # Clinical Risk Factors
+    if factors:
+        elements.append(Paragraph("<b>Clinical Risk Factors:</b>", styles["Heading3"]))
+        for f in factors:
+            elements.append(Paragraph(f"- {f}", styles["Normal"]))
+        elements.append(Spacer(1, 0.2 * inch))
+
+    # Agent Recommendation
+    elements.append(Paragraph("<b>Agent Recommendation:</b>", styles["Heading3"]))
+    elements.append(Paragraph(recommendation, styles["Normal"]))
+    elements.append(Spacer(1, 0.3 * inch))
+
+    # Footer
     elements.append(Paragraph("Generated under HEC Generative AI Training Hackathon", styles["Normal"]))
     elements.append(Paragraph("Team Chinar AI - AJK", styles["Normal"]))
+    elements.append(Spacer(1, 0.2 * inch))
+    elements.append(Paragraph("<i>Disclaimer: This report is AI-generated. Please consult a qualified doctor for clinical decisions.</i>", styles["Normal"]))
 
     doc.build(elements)
     return file_path
-
-
 # Interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
@@ -177,7 +262,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     - Munazza Zahra  
     - Kokub Khurishid  
 
-    © 2026 Team Kohsar AI – All Rights Reserved
+    © 2026 Team Chinar AI – All Rights Reserved
     """)
 
 demo.launch()