Delete app2(working).py

app2(working).py

@@ -1,308 +0,0 @@
-import gradio as gr
-import joblib
-import pandas as pd
-import numpy as np
-import os
-from groq import Groq
-
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table
-from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
-from reportlab.lib import colors
-from reportlab.lib.units import inch
-from reportlab.platypus import TableStyle
-
-# ===============================
-# Load Model
-# ===============================
-model = joblib.load("typhoidguard_model_reduced2.pkl")
-
-# Groq Client
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-# Cache LLM result (avoid double call)
-last_llm_report = ""
-last_basic_output = ""
-
-
-# ===============================
-# 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 of Treatment Failure: {prob_pct}%
-Risk Category: {risk_label}
-
-Clinical Risk Factors:
-{", ".join(factors) if factors else "None"}
-
-Agent Recommendation:
-{recommendation}
-
-Generate a structured detailed report including:
-1. Clinical Interpretation
-2. Possible Complications
-3. Immediate Precautions
-4. Preventative Measures
-5. Follow-up Recommendations
-
-Use professional formatting.
-End with reminder that final decision must be taken by 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
-
-
-# ===============================
-# PREDICT FUNCTION (UPDATED FEATURES)
-# ===============================
-def predict_basic(name, age, gender, platelets, hb, duration,
-                  blood_bacteria, severity, medication):
-
-    global last_llm_report, last_basic_output
-    last_llm_report = ""  # reset
-
-    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)
-
-    # Agentic Override Logic
-    low_platelets = platelets < 150000
-    low_hb = hb < 10
-    prolonged_treatment = duration > 7
-
-    if low_platelets or low_hb:
-        prob_pct = 90.0
-        risk_label = "🔴 HIGH RISK"
-        color = "#d62828"
-        recommendation = "Immediate referral to secondary care facility recommended."
-    elif prob >= 0.65:
-        risk_label = "🔴 HIGH RISK"
-        color = "#d62828"
-        recommendation = "Urgent clinical evaluation required."
-    elif prob >= 0.35:
-        risk_label = "🟡 MODERATE RISK"
-        color = "#f4a261"
-        recommendation = "Close monitoring advised. Repeat tests in 48 hours."
-    else:
-        risk_label = "🟢 LOW RISK"
-        color = "#2a9d8f"
-        recommendation = "Continue standard treatment protocol."
-
-    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 severity == "High":
-        factors.append("High Symptom Severity")
-
-    urdu_summary = f"""
-    <div style='background:#eef7ff;padding:10px;border-radius:8px;'>
-    <b>اردو خلاصہ:</b><br>
-    مریض میں علاج ناکامی کا خطرہ <b>{prob_pct}%</b> ہے۔ موجودہ کیفیت: <b>{risk_label}</b>۔<br>
-    براہ کرم ڈاکٹر سے مشورہ ضرور کریں۔
-    </div>
-    """
-
-    basic_output = 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_summary}
-    </div>
-    """
-
-    last_basic_output = basic_output
-
-    return basic_output
-
-
-# ===============================
-# DETAILED REPORT BUTTON
-# ===============================
-def generate_detailed(name, age, gender, platelets, hb, duration,
-                      blood_bacteria, severity, medication):
-
-    global last_llm_report, last_basic_output
-
-    basic_html = predict_basic(name, age, gender, platelets, hb,
-                               duration, blood_bacteria, severity, medication)
-
-    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)
-
-    factors = []
-    if platelets < 150000:
-        factors.append("Low Platelet Count")
-    if hb < 10:
-        factors.append("Low Hemoglobin")
-
-    patient_info = f"""
-Age: {age}
-Gender: {gender}
-Platelets: {platelets}
-Hemoglobin: {hb}
-Treatment Duration: {duration}
-Blood Bacteria: {blood_bacteria}
-Severity: {severity}
-Medication: {medication}
-"""
-
-    llm_report = generate_llm_report(
-        patient_info, prob_pct, "See Above", "See Above", factors
-    )
-
-    last_llm_report = llm_report
-
-    formatted_llm = llm_report.replace("\n", "<br>")
-
-    return basic_html + f"""
-    <hr>
-    <h3 style="color:#003366;">🧠 Detailed AI Report & Recommendations</h3>
-    <div style="background:#f8f9fa;padding:15px;border-radius:10px;line-height:1.6;">
-    {formatted_llm}
-    </div>
-    """
-
-
-# ===============================
-# PDF GENERATION
-# ===============================
-def generate_pdf(name, age, gender, platelets, hb, duration,
-                 blood_bacteria, severity, medication):
-
-    global last_llm_report, last_basic_output
-
-    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, 0.3 * inch))
-
-    # Basic Output
-    elements.append(Paragraph(last_basic_output.replace("<br>", "<br/>"), styles["Normal"]))
-    elements.append(Spacer(1, 0.3 * inch))
-
-    # Detailed LLM Output (if exists)
-    if last_llm_report:
-        elements.append(Paragraph("<b>Detailed AI Report</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, 0.3 * inch))
-
-    elements.append(Paragraph(
-        "<i>Disclaimer: This report is AI-generated. Please consult a qualified doctor before making clinical decisions.</i>",
-        styles["Normal"]
-    ))
-
-    doc.build(elements)
-    return file_path
-
-
-# ===============================
-# INTERFACE
-# ===============================
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-
-    gr.Markdown("# 🏥 TyphoidGuard AI")
-    gr.Markdown("### HEC Generative AI Training Hackathon – Pakistan")
-
-    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"
-        )
-
-    output = gr.HTML()
-
-    predict_btn = gr.Button("🔍 Basic Risk Assessment")
-    detailed_btn = gr.Button("🧠 Detailed Report and Recommendations")
-    pdf_btn = gr.Button("📄 Generate PDF Report")
-
-    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()