| 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, Image |
| ) |
| from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet |
| from reportlab.lib import colors |
| from reportlab.lib.units import inch |
|
|
| |
| |
| |
| model = joblib.load("typhoidguard_model_reduced2.pkl") |
| client = Groq(api_key=os.environ.get("GROQ_API_KEY")) |
|
|
| last_llm_report = "" |
| last_basic_data = {} |
|
|
| LOGO_PATH = "logo1.png" |
|
|
| |
| |
| |
| 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 this line: "Remember, this advice is generated based on the provided information and should not replace the judgment of a qualified doctor. |
| It is essential to consult a qualified doctor for personalized advice and treatment 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 |
|
|
|
|
| |
| |
| |
| 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, |
| "color": color |
| } |
|
|
| 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: |
| <span style="background:{color};color:white;padding:6px 12px;border-radius:6px;"> |
| {prob_pct}% - {risk_label} |
| </span> |
| </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> |
| """ |
|
|
|
|
| |
| |
| |
| 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"] |
| ) |
|
|
| lines = last_llm_report.split("\n") |
|
|
| formatted_parts = [] |
| disclaimer_html = "" |
|
|
| for line in lines: |
| line = line.strip() |
| if not line: |
| continue |
|
|
| |
| if "consult a qualified doctor" in line.lower(): |
| disclaimer_html = f""" |
| <div style=" |
| margin-top:15px; |
| padding:12px; |
| background:#fff3cd; |
| border-left:5px solid #ff9800; |
| border-radius:6px; |
| font-size:14px;"> |
| <b>Disclaimer:</b><br> |
| {line} |
| </div> |
| """ |
| continue |
|
|
| |
| if line.startswith("##"): |
| clean_heading = line.replace("##", "").strip() |
| formatted_parts.append( |
| f"<br><b style='font-size:16px;color:#003366;'>{clean_heading}:</b><br>" |
| ) |
| else: |
| formatted_parts.append(f"{line}<br>") |
|
|
| formatted = "".join(formatted_parts) |
|
|
| return basic_html + f""" |
| <hr> |
| <h3 style="color:#003366;">Detailed AI Report & Recommendations</h3> |
| <div style="background:#f9f9f9;padding:15px;border-radius:8px;"> |
| {formatted} |
| {disclaimer_html} |
| </div> |
| """ |
|
|
|
|
| |
| |
| |
| 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() |
|
|
| |
| heading_style = ParagraphStyle( |
| name="CustomHeading", |
| parent=styles["Heading2"], |
| fontSize=14, |
| spaceAfter=6, |
| textColor=colors.black |
| ) |
|
|
| bullet_style = ParagraphStyle( |
| name="BulletStyle", |
| parent=styles["Normal"], |
| leftIndent=15, |
| bulletIndent=5, |
| spaceAfter=4 |
| ) |
|
|
| disclaimer_heading_style = ParagraphStyle( |
| name="DisclaimerHeading", |
| parent=styles["Heading2"], |
| textColor=colors.red, |
| fontSize=13, |
| spaceBefore=15 |
| ) |
|
|
| disclaimer_text_style = ParagraphStyle( |
| name="DisclaimerText", |
| parent=styles["Normal"], |
| fontSize=10, |
| textColor=colors.grey |
| ) |
|
|
| |
| if os.path.exists(LOGO_PATH): |
| elements.append(Image(LOGO_PATH, width=1.6*inch, height=1.2*inch)) |
| elements.append(Spacer(1,0.2*inch)) |
|
|
| elements.append(Paragraph("<b>TyphoidGuard AI Clinical Report</b>", styles["Title"])) |
| elements.append(Spacer(1, 0.3 * inch)) |
|
|
| |
| risk_color = colors.HexColor(d["color"]) |
| badge_style = ParagraphStyle( |
| name="Badge", |
| parent=styles["Normal"], |
| backColor=risk_color, |
| textColor=colors.white, |
| fontSize=13, |
| leading=18, |
| leftIndent=10, |
| rightIndent=10, |
| spaceBefore=10, |
| spaceAfter=15 |
| ) |
|
|
| elements.append(Paragraph( |
| f"<b>Risk: {d['prob_pct']}% - {d['risk_label']}</b>", |
| badge_style |
| )) |
| elements.append(Spacer(1,0.3*inch)) |
|
|
| |
| 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"]], |
| ] |
|
|
| table = Table(table_data, colWidths=[2.5*inch, 3*inch]) |
| table.setStyle(TableStyle([ |
| ('GRID',(0,0),(-1,-1),1,colors.grey), |
| ('FONTNAME',(0,0),(-1,-1),'Helvetica') |
| ])) |
|
|
| elements.append(table) |
| elements.append(Spacer(1,0.4*inch)) |
|
|
| |
| elements.append(Paragraph("<b>Agent Recommendation</b>", heading_style)) |
| elements.append(Paragraph(d["recommendation"], styles["Normal"])) |
| elements.append(Spacer(1,0.4*inch)) |
|
|
| |
| if last_llm_report: |
| elements.append(Paragraph("<b>Detailed AI Report</b>", heading_style)) |
| elements.append(Spacer(1,0.2*inch)) |
|
|
| lines = last_llm_report.split("\n") |
|
|
| for line in lines: |
| line = line.strip() |
|
|
| if not line: |
| continue |
|
|
| if "qualified doctor" in line.lower(): |
| continue |
|
|
| if line.startswith("##"): |
| clean_heading = line.replace("##", "").strip() |
| elements.append(Spacer(1,0.2*inch)) |
| elements.append( |
| Paragraph(f"<b>{clean_heading}:</b>", heading_style) |
| ) |
| else: |
| elements.append(Paragraph(line, styles["Normal"])) |
|
|
| elements.append(Spacer(1,0.5*inch)) |
|
|
| |
| elements.append(Paragraph("Disclaimer", disclaimer_heading_style)) |
| elements.append(Spacer(1,0.1*inch)) |
| elements.append(Paragraph( |
| "Remember to consult a qualified doctor for personalized advice and treatment. " |
| "They will be able to provide guidance tailored to the patient's specific needs and circumstances.", |
| disclaimer_text_style |
| )) |
|
|
| elements.append(Spacer(1,0.5*inch)) |
|
|
| |
| elements.append(Paragraph("<b>Team Chinar AI (AJK)</b>", styles["Normal"])) |
| elements.append(Paragraph( |
| "Anees Qumar Abbasi (Team Lead) | Sharafat Hussain | Salma Asghar | " |
| "Kaleem Hussain | Munazza Zahra | Kokub Khhurishid", |
| styles["Normal"] |
| )) |
|
|
| doc.build(elements) |
|
|
| |
| |
| gr.Info("Report Generated Successfully! Please download it below.") |
|
|
| return file_path |
| |
| |
| |
| |
| |
| |
| with gr.Blocks( |
| theme=gr.themes.Soft(), |
| css=""" |
| body { |
| background: linear-gradient(135deg, #e3f2fd, #f4f9ff); |
| } |
| |
| .center-header { |
| text-align: center; |
| } |
| |
| #header-center { |
| text-align: center; |
| } |
| |
| .instruction-text { |
| text-align: center; |
| font-size: 16px; |
| color: #37474f; |
| margin-bottom: 20px; |
| } |
| |
| .gr-button { |
| border-radius: 10px !important; |
| font-weight: 600 !important; |
| padding: 10px 18px !important; |
| font-size: 15px !important; |
| } |
| |
| .gr-textbox, .gr-number, .gr-dropdown { |
| border-radius: 8px !important; |
| } |
| |
| .footer-team { |
| background: #ffffff; |
| padding: 15px; |
| border-radius: 10px; |
| margin-top: 20px; |
| text-align: center; |
| font-size: 14px; |
| box-shadow: 0 2px 8px rgba(0,0,0,0.05); |
| } |
| """ |
| ) as demo: |
|
|
| |
| |
| '''gr.Markdown('<div style="text-align:center;">') |
| gr.Image(LOGO_PATH, width=300, show_label=False, container=False) |
| gr.Markdown('</div>') |
| # ---- CENTERED HEADER ---- |
| gr.Markdown(""" |
| <div class="center-header"> |
| <h1 style="color:#0d47a1;">🏥 TyphoidGuard AI</h1> |
| <h4 style="color:#37474f;">HEC Generative AI Hackathon – Pakistan</h4> |
| <h4 style="color:#00695c;">Team Chinar AI (AJK)</h4> |
| </div> |
| """)''' |
| |
| |
| with gr.Row(): |
| with gr.Column(scale=1, elem_id="header-center"): |
| gr.Image(LOGO_PATH, width=300, show_label=False) |
| gr.Markdown(""" |
| <div style="text-align:center;"> |
| <h1 style="color:#0d47a1; margin-top:15px;">🏥 TyphoidGuard AI</h1> |
| <h4 style="color:#37474f; margin:5px 0;">HEC Generative AI Hackathon – Pakistan</h4> |
| <h4 style="color:#00695c; margin:5px 0;">Team Chinar AI (AJK)</h4> |
| </div> |
| """) |
|
|
| |
| gr.Markdown(""" |
| <div class="instruction-text"> |
| Please enter the patient clinical values below to assess |
| the risk of typhoid treatment failure and receive AI-based recommendations. |
| </div> |
| """) |
|
|
| |
| 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" |
| ) |
|
|
| |
| with gr.Row(): |
| predict_btn = gr.Button("Basic Risk Assessment") |
| detailed_btn = gr.Button("Detailed Report & Recommendations") |
| 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()) |
|
|
| |
| gr.Markdown(""" |
| <div class="footer-team"> |
| <b>Team Chinar AI (AJK)</b><br> |
| Anees Qumar Abbasi (Team Lead) | Sharafat Hussain | Salma Asghar | |
| Kaleem Hussain | Munazza Zahra | Kokub Khurishid |
| </div> |
| """) |
|
|
| demo.launch() |
