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app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.lib import colors
+from reportlab.lib.units import inch
+from reportlab.platypus import Table
+from reportlab.platypus import TableStyle
+
+# Load model
+model = joblib.load("typhoidguard_model_reduced.pkl")
+
+# Risk & Agent Logic
+def predict_and_generate(name, age, gender, platelets, hb, duration, calcium, potassium):
+
+    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
+    }])
+
+    risk = model.predict_proba(data)[0][1] * 100
+    risk = round(risk, 2)
+
+    # Agent Reasoning
+    factors = []
+    if platelets < 150000:
+        factors.append("Low Platelet Count")
+    if hb < 10:
+        factors.append("Low Hemoglobin")
+    if duration > 7:
+        factors.append("Prolonged Treatment Duration")
+
+    if risk > 65:
+        status = "🔴 HIGH RISK"
+        recommendation = "Immediate referral to secondary care facility recommended. Close monitoring required."
+        color = "#d62828"
+    elif 35 <= risk <= 65:
+        status = "🟡 MODERATE RISK"
+        recommendation = "Close monitoring advised. Repeat blood tests within 48 hours."
+        color = "#f4a261"
+    else:
+        status = "🟢 LOW RISK"
+        recommendation = "Continue standard treatment protocol."
+        color = "#2a9d8f"
+
+    explanation = f"""
+    <div style="font-family:Arial; padding:15px;">
+        <h2 style="color:{color};">Patient: {name}</h2>
+        <h3>Risk of Treatment Failure: {risk}%</h3>
+        <h3>Status: {status}</h3>
+        <h4>Clinical Risk Factors:</h4>
+        <ul>
+            {''.join([f"<li>{f}</li>" for f in factors])}
+        </ul>
+        <h4>Agent Recommendation:</h4>
+        <p>{recommendation}</p>
+    </div>
+    """
+
+    return explanation
+
+
+# PDF Generation
+def generate_pdf(name, age, gender, platelets, hb, duration, calcium, potassium):
+
+    file_path = f"{name}_Typhoid_Report.pdf"
+    doc = SimpleDocTemplate(file_path)
+    elements = []
+    styles = getSampleStyleSheet()
+
+    elements.append(Paragraph("<b>TyphoidGuard AI Clinical Report</b>", styles["Title"]))
+    elements.append(Spacer(1, 0.5 * inch))
+
+    data = [
+        ["Patient Name", name],
+        ["Age", age],
+        ["Gender", gender],
+        ["Platelet Count", platelets],
+        ["Hemoglobin", hb],
+        ["Treatment Duration", duration],
+        ["Calcium", calcium],
+        ["Potassium", potassium],
+    ]
+
+    table = Table(data)
+    table.setStyle(TableStyle([
+        ('BACKGROUND', (0,0), (-1,0), colors.lightblue),
+        ('GRID', (0,0), (-1,-1), 1, colors.grey)
+    ]))
+
+    elements.append(table)
+    elements.append(Spacer(1, 0.5 * inch))
+    elements.append(Paragraph("Generated under HEC Generative AI Training Hackathon", styles["Normal"]))
+    elements.append(Paragraph("Team Kohsar AI - AJK", styles["Normal"]))
+
+    doc.build(elements)
+    return file_path
+
+
+# Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("""
+    # 🏥 TyphoidGuard AI
+    ### HEC Generative AI Training Hackathon – Pakistan
+    #### Team Kohsar AI (AJK)
+    """)
+
+    with gr.Row():
+        name = gr.Textbox(label="Patient Name")
+        age = gr.Number(label="Age")
+        gender = gr.Radio(["Male", "Female"], label="Gender")
+
+    with gr.Row():
+        platelets = gr.Number(label="Platelet Count")
+        hb = gr.Number(label="Hemoglobin (g/dL)")
+        duration = gr.Number(label="Treatment Duration (Days)")
+
+    with gr.Row():
+        calcium = gr.Number(label="Calcium (mg/dL)")
+        potassium = gr.Number(label="Potassium (mmol/L)")
+
+    output = gr.HTML()
+
+    predict_btn = gr.Button("🔍 Analyze Patient")
+    pdf_btn = gr.Button("📄 Generate PDF Report")
+
+    predict_btn.click(
+        predict_and_generate,
+        inputs=[name, age, gender, platelets, hb, duration, calcium, potassium],
+        outputs=output
+    )
+
+    pdf_btn.click(
+        generate_pdf,
+        inputs=[name, age, gender, platelets, hb, duration, calcium, potassium],
+        outputs=gr.File()
+    )
+
+    gr.Markdown("""
+    ---
+    ### 👥 Team Members:
+    - Anees Qumar Abbasi (Team Lead)
+    - Sharafat Hussain  
+    - Salma Asghar  
+    - Kaleem Hussain  
+    - Munazza Zahra  
+    - Kokub Khurishid  
+
+    © 2026 Team Kohsar AI – All Rights Reserved
+    """)
+
+demo.launch()

requirements.txt

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+gradio
+scikit-learn
+pandas
+numpy
+reportlab
+joblib

typhoidguard_model_reduced.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3da4eb8f010a4d05b23d554b738eb6756acdafe2eb78766c85f10829e719c85
+size 124971