Delete app11.py

app11.py

@@ -1,243 +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
-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")
-
-# Groq Client (API key from HF Secrets)
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-# ===============================
-# LLM REPORT 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 clear bullet points.
-End with a reminder that final decisions must be made by a 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
-
-
-# ===============================
-# Risk & Agentic Logic (UNCHANGED)
-# ===============================
-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
-    }])
-
-    X_processed = model.named_steps["preprocessor"].transform(data)
-    prob = model.named_steps["classifier"].predict_proba(X_processed)[0][1]
-
-    low_platelets = platelets < 150000
-    low_hb = hb < 10
-    prolonged_treatment = duration > 7
-
-    if low_platelets or low_hb:
-        prob_pct = 90.0
-    else:
-        prob_pct = round(prob * 100, 2)
-
-    if prob > 0.65:
-        risk_label = "🔴 HIGH RISK"
-        color = "#d62828"
-    elif prob >= 0.35:
-        risk_label = "🟡 MODERATE RISK"
-        color = "#f4a261"
-    else:
-        risk_label = "🟢 LOW RISK"
-        color = "#2a9d8f"
-
-    if low_platelets or low_hb:
-        risk_label = "🔴 HIGH RISK"
-        color = "#d62828"
-        recommendation = "Immediate referral to secondary care facility recommended. Close monitoring required."
-    elif prob >= 0.35:
-        recommendation = "Close monitoring advised. Repeat blood tests within 48 hours."
-    else:
-        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 calcium < 8.5:
-        factors.append("Low Calcium")
-    if potassium < 3.5 or potassium > 5.0:
-        factors.append("Abnormal Potassium")
-
-    # -------- LLM CALL --------
-    patient_info = f"""
-Age: {age}
-Gender: {gender}
-Platelets: {platelets}
-Hemoglobin: {hb}
-Treatment Duration: {duration}
-Calcium: {calcium}
-Potassium: {potassium}
-"""
-
-    llm_report = generate_llm_report(
-        patient_info, prob_pct, risk_label, recommendation, factors
-    )
-
-    # -------- HTML OUTPUT --------
-    explanation = 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>
-
-        <hr>
-
-        <h3 style="color:#003366;">🧠 Detailed AI Report & Recommendations</h3>
-        <div style="background:#f8f9fa; padding:15px; border-radius:10px; line-height:1.6;">
-            {llm_report.replace("\n", "<br>")}
-        </div>
-    </div>
-    """
-
-    return explanation
-
-
-# ===============================
-# PDF Generation (LLM Included)
-# ===============================
-def generate_pdf(name, age, gender, platelets, hb, duration, calcium, potassium):
-
-    html_output = predict_and_generate(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, 0.3 * inch))
-
-    elements.append(Paragraph(html_output.replace("<br>", "<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
-    ### HEC Generative AI Training 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():
-        calcium = gr.Number(label="Calcium (mg/dL)")
-        potassium = gr.Number(label="Potassium (mmol/L)")
-
-    output = gr.HTML()
-
-    predict_btn = gr.Button("🧠 Detailed Report and Recommendations")
-    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 Chinar AI – All Rights Reserved
-    """)
-
-demo.launch()