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 getSampleStyleSheet, ParagraphStyle
+from reportlab.lib import colors
+from reportlab.lib.units import inch
+
+# ===============================
+# Load ML Model
+# ===============================
+model = joblib.load("typhoidguard_model_reduced.pkl")
+
+# ===============================
+# Groq Client
+# ===============================
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# ===============================
+# GLOBAL CACHE (avoid double LLM call)
+# ===============================
+LLM_CACHE = {
+    "english": "",
+    "urdu": ""
+}
+
+# ===============================
+# FORMAT LLM TEXT TO HTML
+# ===============================
+def format_llm_text(text):
+    lines = text.split("\n")
+    html = ""
+
+    for line in lines:
+        line = line.strip()
+
+        if line.lower().startswith("1.") or line.lower().startswith("2.") or line.lower().startswith("3.") \
+           or line.lower().startswith("4.") or line.lower().startswith("5."):
+            html += f"<h3 style='color:#003366'>{line}</h3>"
+
+        elif line.startswith("*"):
+            html += f"<li>{line[1:].strip()}</li>"
+
+        elif line:
+            html += f"<p>{line}</p>"
+
+    return f"<ul>{html}</ul>"
+
+
+# ===============================
+# LLM REPORT (English + Urdu)
+# ===============================
+def generate_llm_report(patient_info, prob_pct, risk_label, recommendation, factors):
+
+    prompt = f"""
+You are a medical AI assistant helping doctors in rural Pakistan.
+
+Patient Details:
+{patient_info}
+
+Risk of Treatment Failure: {prob_pct}%
+Risk Category: {risk_label}
+
+Risk Factors:
+{", ".join(factors) if factors else "None"}
+
+Agent Recommendation:
+{recommendation}
+
+TASK:
+1. Write a detailed medical report with headings:
+   - Clinical Interpretation
+   - Possible Complications
+   - Immediate Precautions
+   - Preventive Measures
+   - Follow-up Recommendations
+
+2. Then write a SHORT summary in URDU for healthcare staff.
+
+Rules:
+- Use bullet points
+- No markdown (** or ###)
+- Simple clinical language
+"""
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.3,
+    )
+
+    full_text = response.choices[0].message.content
+
+    # Split English & Urdu
+    if "اردو" in full_text:
+        eng, urdu = full_text.split("اردو", 1)
+        urdu = "اردو" + urdu
+    else:
+        eng, urdu = full_text, ""
+
+    return eng.strip(), urdu.strip()
+
+
+# ===============================
+# BASIC ANALYSIS (NO LLM)
+# ===============================
+def analyze_patient(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
+    }])
+
+    X = model.named_steps["preprocessor"].transform(data)
+    prob = model.named_steps["classifier"].predict_proba(X)[0][1]
+
+    low_platelets = platelets < 150000
+    low_hb = hb < 10
+
+    prob_pct = 90.0 if (low_platelets or low_hb) else round(prob * 100, 2)
+
+    if low_platelets or low_hb:
+        status = "🔴 HIGH RISK"
+        color = "#d62828"
+        recommendation = "Immediate referral to secondary care facility recommended."
+    elif prob >= 0.35:
+        status = "🟡 MODERATE RISK"
+        color = "#f4a261"
+        recommendation = "Close monitoring advised."
+    else:
+        status = "🟢 LOW RISK"
+        color = "#2a9d8f"
+        recommendation = "Continue standard treatment."
+
+    factors = []
+    if low_platelets: factors.append("Low Platelet Count")
+    if low_hb: factors.append("Low Hemoglobin")
+
+    html = f"""
+    <div style="padding:15px;font-family:Arial">
+        <h2 style="color:{color}">Patient: {name}</h2>
+        <h3>Risk of Treatment Failure: {prob_pct}%</h3>
+        <h3>Status: {status}</h3>
+        <h4>Agent Recommendation</h4>
+        <p><b>{recommendation}</b></p>
+    </div>
+    """
+
+    return html, prob_pct, status, recommendation, factors
+
+
+# ===============================
+# DETAILED REPORT BUTTON
+# ===============================
+def detailed_report(name, age, gender, platelets, hb, duration, calcium, potassium):
+
+    html, prob_pct, status, rec, factors = analyze_patient(
+        name, age, gender, platelets, hb, duration, calcium, potassium
+    )
+
+    patient_info = f"""
+Age: {age}, Gender: {gender}
+Platelets: {platelets}, Hb: {hb}
+Duration: {duration} days
+Calcium: {calcium}, Potassium: {potassium}
+"""
+
+    eng, urdu = generate_llm_report(patient_info, prob_pct, status, rec, factors)
+
+    LLM_CACHE["english"] = eng
+    LLM_CACHE["urdu"] = urdu
+
+    formatted_eng = format_llm_text(eng)
+
+    return html + f"""
+    <hr>
+    <h2 style="color:#003366">🧠 Detailed AI Report</h2>
+    {formatted_eng}
+    <h3 style="color:#006400">اردو خلاصہ</h3>
+    <p>{urdu}</p>
+    """
+
+
+# ===============================
+# PDF GENERATION (USES CACHE)
+# ===============================
+def generate_pdf(name, age, gender, platelets, hb, duration, calcium, potassium):
+
+    file_path = f"{name}_Typhoid_Report.pdf"
+    doc = SimpleDocTemplate(file_path)
+    styles = getSampleStyleSheet()
+    elements = []
+
+    elements.append(Paragraph("<b>TyphoidGuard AI Clinical Report</b>", styles["Title"]))
+    elements.append(Spacer(1, 0.3 * inch))
+
+    elements.append(Paragraph("<b>Detailed AI Clinical Report</b>", styles["Heading2"]))
+    elements.append(Paragraph(LLM_CACHE["english"].replace("\n", "<br/>"), styles["Normal"]))
+    elements.append(Spacer(1, 0.2 * inch))
+
+    elements.append(Paragraph("<b>اردو خلاصہ</b>", styles["Heading2"]))
+    elements.append(Paragraph(LLM_CACHE["urdu"], styles["Normal"]))
+
+    elements.append(Spacer(1, 0.3 * inch))
+    elements.append(Paragraph(
+        "<i>Disclaimer: This report is AI-generated. Consult a qualified doctor.</i>",
+        styles["Normal"]
+    ))
+
+    doc.build(elements)
+    return file_path
+
+
+# ===============================
+# UI
+# ===============================
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("# 🏥 TyphoidGuard AI")
+
+    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")
+        duration = gr.Number(label="Treatment Duration")
+
+    with gr.Row():
+        calcium = gr.Number(label="Calcium")
+        potassium = gr.Number(label="Potassium")
+
+    output = gr.HTML()
+
+    btn1 = gr.Button("🔍 Analyze Patient")
+    btn2 = gr.Button("🧠 Detailed Report and Recommendations")
+    pdf_btn = gr.Button("📄 Generate PDF Report")
+
+    btn1.click(analyze_patient, 
+               inputs=[name, age, gender, platelets, hb, duration, calcium, potassium],
+               outputs=[output, gr.State(), gr.State(), gr.State(), gr.State()])
+
+    btn2.click(detailed_report,
+               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())
+
+demo.launch()