Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import os
+from groq import Groq
+
+from reportlab.platypus import (
+    SimpleDocTemplate, Paragraph, Spacer,
+    Table, TableStyle
+)
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.lib import colors
+from reportlab.lib.units import inch
+
+# ===============================
+# LOAD MODEL
+# ===============================
+model = joblib.load("typhoidguard_model_reduced.pkl")
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+last_llm_report = ""
+last_basic_data = {}
+
+# ===============================
+# LLM FUNCTION
+# ===============================
+def generate_llm_report(patient_info, prob_pct, risk_label, recommendation, factors):
+
+    prompt = f"""
+You are a responsible medical AI assistant helping rural healthcare units in Pakistan.
+
+Patient Details:
+{patient_info}
+
+Predicted Risk: {prob_pct}%
+Risk Category: {risk_label}
+
+Clinical Risk Factors:
+{", ".join(factors) if factors else "None"}
+
+Agent Recommendation:
+{recommendation}
+
+Generate structured sections:
+Clinical Interpretation
+Possible Complications
+Immediate Precautions
+Preventative Measures
+Follow-up Recommendations
+
+Keep headings clear.
+End with reminder to consult qualified doctor.
+"""
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.4,
+    )
+
+    return response.choices[0].message.content
+
+
+# ===============================
+# BASIC PREDICTION
+# ===============================
+def predict_basic(name, age, gender, platelets, hb, duration,
+                  blood_bacteria, severity, medication):
+
+    global last_basic_data, last_llm_report
+    last_llm_report = ""
+
+    data = pd.DataFrame([{
+        "Age": age,
+        "Platelet Count": platelets,
+        "Hemoglobin (g/dL)": hb,
+        "Treatment Duration": duration,
+        "Gender": gender,
+        "Blood Culture Bacteria": blood_bacteria,
+        "Symptoms Severity": severity,
+        "Current Medication": medication
+    }])
+
+    prob = model.predict_proba(data)[0][1]
+    prob_pct = round(prob * 100, 2)
+
+    low_platelets = platelets < 150000
+    low_hb = hb < 10
+
+    if low_platelets or low_hb:
+        prob_pct = 90.0
+        risk_label = "HIGH RISK"
+        color = "#b00020"
+        recommendation = "Immediate referral to secondary care facility recommended."
+    elif prob >= 0.65:
+        risk_label = "HIGH RISK"
+        color = "#b00020"
+        recommendation = "Urgent clinical evaluation required."
+    elif prob >= 0.35:
+        risk_label = "MODERATE RISK"
+        color = "#f57c00"
+        recommendation = "Close monitoring advised."
+    else:
+        risk_label = "LOW RISK"
+        color = "#2e7d32"
+        recommendation = "Continue standard treatment protocol."
+
+    factors = []
+    if low_platelets:
+        factors.append("Low Platelet Count")
+    if low_hb:
+        factors.append("Low Hemoglobin")
+
+    last_basic_data = {
+        "name": name,
+        "age": age,
+        "gender": gender,
+        "platelets": platelets,
+        "hb": hb,
+        "duration": duration,
+        "bacteria": blood_bacteria,
+        "severity": severity,
+        "medication": medication,
+        "prob_pct": prob_pct,
+        "risk_label": risk_label,
+        "recommendation": recommendation,
+        "factors": factors
+    }
+
+    urdu = f"""
+    <div style='background:#eef3fb;padding:10px;border-radius:8px;'>
+    <b>اردو خلاصہ:</b><br>
+    علاج ناکامی کا خطرہ <b>{prob_pct}%</b> ہے۔ درجہ: <b>{risk_label}</b>۔<br>
+    ڈاکٹر سے مشورہ ضرور کریں۔
+    </div>
+    """
+
+    return f"""
+    <div style="font-family:Arial;padding:15px;">
+    <h2 style="color:{color};">Patient: {name}</h2>
+    <h3>Risk of Treatment Failure: {prob_pct}%</h3>
+    <h3>Status: {risk_label}</h3>
+
+    <h4>Clinical Risk Factors:</h4>
+    <ul>
+    {''.join([f"<li>{f}</li>" for f in factors]) if factors else "<li>None</li>"}
+    </ul>
+
+    <h4>Agent Recommendation:</h4>
+    <p><b>{recommendation}</b></p>
+
+    {urdu}
+    </div>
+    """
+
+
+# ===============================
+# DETAILED BUTTON
+# ===============================
+def generate_detailed(*inputs):
+    global last_basic_data, last_llm_report
+
+    basic_html = predict_basic(*inputs)
+
+    d = last_basic_data
+
+    patient_info = f"""
+Age: {d['age']}
+Gender: {d['gender']}
+Platelets: {d['platelets']}
+Hemoglobin: {d['hb']}
+Duration: {d['duration']}
+Bacteria: {d['bacteria']}
+Severity: {d['severity']}
+Medication: {d['medication']}
+"""
+
+    last_llm_report = generate_llm_report(
+        patient_info,
+        d["prob_pct"],
+        d["risk_label"],
+        d["recommendation"],
+        d["factors"]
+    )
+
+    formatted = last_llm_report.replace("\n", "<br>")
+
+    return basic_html + f"""
+    <hr>
+    <h3 style="color:#003366;">Detailed AI Report & Recommendations</h3>
+    <div style="background:#f9f9f9;padding:15px;border-radius:8px;">
+    {formatted}
+    </div>
+    """
+
+
+# ===============================
+# PDF GENERATION (PROFESSIONAL)
+# ===============================
+def generate_pdf(*inputs):
+
+    global last_basic_data, last_llm_report
+
+    d = last_basic_data
+    file_path = f"{d['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, 0.3 * inch))
+
+    # ---- TABLE ----
+    table_data = [
+        ["Patient Name", d["name"]],
+        ["Age", d["age"]],
+        ["Gender", d["gender"]],
+        ["Platelet Count", d["platelets"]],
+        ["Hemoglobin", d["hb"]],
+        ["Treatment Duration", d["duration"]],
+        ["Bacteria", d["bacteria"]],
+        ["Severity", d["severity"]],
+        ["Medication", d["medication"]],
+        ["Predicted Risk %", d["prob_pct"]],
+        ["Risk Level", d["risk_label"]],
+    ]
+
+    table = Table(table_data, colWidths=[2.5*inch, 3*inch])
+    table.setStyle(TableStyle([
+        ('BACKGROUND',(0,0),(-1,0),colors.lightgrey),
+        ('GRID',(0,0),(-1,-1),1,colors.grey),
+        ('FONTNAME',(0,0),(-1,-1),'Helvetica')
+    ]))
+
+    elements.append(table)
+    elements.append(Spacer(1,0.4*inch))
+
+    # ---- BASIC RECOMMENDATION ----
+    elements.append(Paragraph("<b>Agent Recommendation</b>", styles["Heading2"]))
+    elements.append(Spacer(1,0.2*inch))
+    elements.append(Paragraph(d["recommendation"], styles["Normal"]))
+    elements.append(Spacer(1,0.4*inch))
+
+    # ---- LLM SECTION ----
+    if last_llm_report:
+        elements.append(Paragraph("<b>Detailed AI Report & Recommendations</b>", styles["Heading2"]))
+        elements.append(Spacer(1,0.2*inch))
+        elements.append(Paragraph(last_llm_report.replace("\n","<br/>"), styles["Normal"]))
+        elements.append(Spacer(1,0.4*inch))
+
+    # Disclaimer
+    elements.append(Paragraph(
+        "<i>This report is AI-generated. Final medical decisions must be made by a qualified doctor.</i>",
+        styles["Normal"]
+    ))
+
+    # Team Footer
+    elements.append(Spacer(1,0.5*inch))
+    elements.append(Paragraph("<b>Team Chinar AI (AJK)</b>", styles["Normal"]))
+    elements.append(Paragraph(
+        "Anees Qumar Abbasi | Sharafat Hussain | Salma Asghar | "
+        "Kaleem Hussain | Munazza Zahra | Kokub Khurishid",
+        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 Hackathon – Pakistan  
+    #### Team Chinar 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():
+        blood_bacteria = gr.Dropdown(
+            ["Escherichia coli","Salmonella typhi","Unknown","Staphylococcus"],
+            label="Blood Culture Bacteria"
+        )
+        severity = gr.Dropdown(
+            ["Low","Moderate","High","Unknown"],
+            label="Symptoms Severity"
+        )
+        medication = gr.Dropdown(
+            ["Amoxicillin","Ceftriaxone","Azithromycin"],
+            label="Current Medication"
+        )
+
+    # ---- BUTTONS ABOVE OUTPUT ----
+    with gr.Row():
+        predict_btn = gr.Button("Basic Risk Assessment", variant="primary")
+        detailed_btn = gr.Button("Detailed Report & Recommendations", variant="secondary")
+        pdf_btn = gr.Button("Generate PDF Report")
+
+    output = gr.HTML()
+
+    predict_btn.click(predict_basic,
+        inputs=[name,age,gender,platelets,hb,duration,
+                blood_bacteria,severity,medication],
+        outputs=output)
+
+    detailed_btn.click(generate_detailed,
+        inputs=[name,age,gender,platelets,hb,duration,
+                blood_bacteria,severity,medication],
+        outputs=output)
+
+    pdf_btn.click(generate_pdf,
+        inputs=[name,age,gender,platelets,hb,duration,
+                blood_bacteria,severity,medication],
+        outputs=gr.File())
+
+demo.launch()