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6fd9abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 | import matplotlib
matplotlib.use('Agg') # Required for server-side plotting
import matplotlib.pyplot as plt
import matplotlib.patheffects as pe
import numpy as np
import io
import base64
import requests
from xhtml2pdf import pisa
from django.template import Context, Template
def get_base64_image(url):
"""Fetches image from URL and converts to base64 for PDF embedding."""
if not url: return None
try:
response = requests.get(url, timeout=5)
if response.status_code == 200:
return f"data:image/jpeg;base64,{base64.b64encode(response.content).decode('utf-8')}"
except Exception as e:
print(f"Error fetching image: {e}")
return None
return None
def generate_scatter_plot(model_conf, symptom_score):
"""
Generates a Linear Regression style plot with a SCIENTIFIC GRID.
"""
# 1. Setup Figure
fig, ax = plt.subplots(figsize=(6, 4), dpi=300)
# 2. Formal Gridding (Scientific Look)
ax.minorticks_on()
ax.grid(which='major', linestyle='-', linewidth=0.6, color='#cbd5e1', alpha=0.8, zorder=0)
ax.grid(which='minor', linestyle=':', linewidth=0.4, color='#e2e8f0', alpha=0.5, zorder=0)
# Clean Spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#64748b')
ax.spines['bottom'].set_color('#64748b')
# 3. Generate Population Trend (Green Line)
np.random.seed(42)
pop_x = np.linspace(5, 95, 150)
pop_y = (pop_x * 0.3) + np.random.normal(0, 5, 150)
pop_y = np.clip(pop_y, 0, 100)
# Plot Population (Green Dots)
ax.scatter(
pop_x,
pop_y,
c='#2ecc71', # Green
s=50,
alpha=0.5,
label='Normal Population',
zorder=5,
edgecolors='none'
)
# Visual "Regression Line"
ax.plot(pop_x, pop_x * 0.3, color='#27ae60', linestyle='--', alpha=0.6, linewidth=1.5, zorder=4)
# 4. Patient Dot (Red, Off the Line based on Risk)
ax.scatter(
[symptom_score],
[model_conf],
c='#ef4444', # Red
s=50,
zorder=10,
label='Patient Result',
edgecolors='black',
linewidth=1
)
# 5. Visual Guide for Distance
normal_y_at_x = symptom_score * 0.3
ax.plot(
[symptom_score, symptom_score],
[model_conf, normal_y_at_x],
color='#ef4444',
linestyle=':',
alpha=0.5,
zorder=9
)
# 6. Labels & Regions
ax.set_xlabel('Symptom Severity', fontsize=9, fontweight='bold', color='#475569')
ax.set_ylabel('AI Risk Score', fontsize=9, fontweight='bold', color='#475569')
ax.legend(loc='upper left', fontsize=8, framealpha=0.9, edgecolor='#cbd5e1')
# Scaling
ax.set_xlim(0, 105)
ax.set_ylim(0, 105)
# 7. Save
buffer = io.BytesIO()
plt.tight_layout()
plt.savefig(buffer, format='png', facecolor='white', edgecolor='none')
buffer.seek(0)
plt.close(fig)
return f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
def generate_medical_pdf(test_result):
# --- 1. Data Calculation ---
model_conf = float(test_result.confidence_score)
symptoms = test_result.symptoms_data or {}
yes_count = sum(1 for ans in symptoms.values() if isinstance(ans, str) and ans.lower() == 'yes')
symptom_score = (yes_count / 8) * 100
mean_score = (model_conf + symptom_score) / 2
is_positive = test_result.result == 'Positive'
# --- DYNAMIC RISK RECALCULATION (Fix for PDF) ---
# This ensures old "Low" records show as "Medium" if score is 50-80%
if is_positive:
if model_conf > 80:
current_risk_level = "High"
elif model_conf >= 50:
current_risk_level = "Medium"
else:
current_risk_level = "Low"
else:
current_risk_level = "Low"
# ------------------------------------------------
# --- 2. Theme Configuration ---
theme_color = '#3498db'
accent_color = '#60a5fa'
light_bg = '#eff6ff'
status_text = 'POSITIVE FOR ABNORMALITIES' if is_positive else 'NO ABNORMALITIES DETECTED'
# --- 3. Fetch Assets ---
xray_img_b64 = get_base64_image(test_result.xray_image_url)
scatter_plot_b64 = generate_scatter_plot(model_conf, symptom_score)
# --- 4. Medication Logic ---
if is_positive:
meds_title = "Suggested Clinical Protocol"
meds_note = "Standard First-Line Regimen (Requires Prescription)"
meds_list = [
{'name': 'Isoniazid (H)', 'dose': '5 mg/kg', 'desc': 'Primary antibiotic for treatment'},
{'name': 'Rifampicin (R)', 'dose': '10 mg/kg', 'desc': 'Broad-spectrum antibiotic'},
{'name': 'Pyrazinamide (Z)', 'dose': '25 mg/kg', 'desc': 'Sterilizing agent'},
{'name': 'Ethambutol (E)', 'dose': '15 mg/kg', 'desc': 'Bacteriostatic agent'}
]
else:
meds_title = "Preventive Recommendations"
meds_note = "Nutritional Support for Respiratory Health"
meds_list = [
{'name': 'Vitamin D3', 'dose': '1000 IU', 'desc': 'Immune modulation support'},
{'name': 'Vitamin C', 'dose': '500 mg', 'desc': 'Antioxidant cellular protection'},
{'name': 'Zinc Gluconate', 'dose': '50 mg', 'desc': 'Immune defense enhancement'}
]
# --- 5. HTML Template ---
html_string = """
<!DOCTYPE html>
<html>
<head>
<style>
@page {
size: A4;
margin: 1.5cm;
margin-bottom: 2.5cm;
@frame footer_frame {
-pdf-frame-content: footerContent;
bottom: 0cm;
left: 1.5cm;
right: 1.5cm;
height: 1.5cm;
}
}
body {
font-family: 'Helvetica', sans-serif;
color: #334155;
font-size: 10px;
line-height: 1.5;
}
/* Header */
.header { border-bottom: 2px solid {{ theme_color }}; padding-bottom: 15px; margin-bottom: 25px; text-align: center; }
.brand-name { font-size: 24px; font-weight: bold; color: {{ theme_color }}; letter-spacing: 0.5px; }
.report-meta { color: #64748b; font-size: 9px; margin-top: 5px; }
/* Headings */
.section-label {
font-size: 10px; font-weight: bold; color: #94a3b8;
text-transform: uppercase; letter-spacing: 1px;
margin-top: 20px; margin-bottom: 8px;
border-bottom: 1px solid #e2e8f0; padding-bottom: 2px;
}
/* Tables & Gridding */
.info-table { width: 100%; margin-bottom: 15px; border-collapse: collapse; border: 1px solid #e2e8f0; }
.info-table td { padding: 8px; vertical-align: top; border: 1px solid #e2e8f0; }
.info-label { font-size: 8px; color: #64748b; text-transform: uppercase; margin-bottom: 2px; }
.info-value { font-size: 11px; font-weight: bold; color: #0f172a; }
.visual-table { width: 100%; margin-bottom: 20px; border-spacing: 0; }
.visual-table td { vertical-align: top; width: 50%; padding: 5px; }
.image-box {
border: 1px solid #e2e8f0; padding: 5px; border-radius: 2px;
text-align: center; height: 200px; overflow: hidden; background: #f8fafc;
}
.chart-box {
text-align: center; height: 200px; display: flex; align-items: center; justify-content: center;
border: 1px solid #e2e8f0; border-radius: 2px; padding: 5px;
}
.result-container {
background-color: {{ light_bg }}; border: 1px solid {{ accent_color }};
padding: 15px; margin-bottom: 20px; border-radius: 4px; text-align: center;
}
.result-main { font-size: 14px; font-weight: bold; color: {{ theme_color }}; margin-bottom: 3px; }
.result-sub { font-size: 10px; color: #475569; }
.metrics-table { width: 100%; border-collapse: collapse; margin-bottom: 20px; border: 1px solid #e2e8f0; }
.metrics-table th { text-align: left; color: #64748b; font-size: 8px; text-transform: uppercase; border: 1px solid #cbd5e1; padding: 6px; background-color: #f8fafc; }
.metrics-table td { padding: 6px; border: 1px solid #e2e8f0; color: #334155; }
.score-highlight { font-weight: bold; color: {{ theme_color }}; font-size: 12px; }
.meds-table { width: 100%; border-collapse: collapse; border: 1px solid #e2e8f0; }
.meds-table th { text-align: left; background-color: #f8fafc; color: #475569; font-size: 8px; font-weight: bold; padding: 6px; border: 1px solid #e2e8f0; }
.meds-table td { padding: 6px; border: 1px solid #e2e8f0; }
.dose-badge { background-color: #e2e8f0; color: #334155; padding: 2px 5px; border-radius: 3px; font-size: 8px; font-weight: bold; }
/* DISCLAIMER */
.disclaimer-box {
margin-top: 30px;
text-align: center;
font-size: 8px;
color: #64748b;
border-top: 1px solid #e2e8f0;
padding-top: 10px;
}
/* FOOTER CONTENT */
#footerContent {
text-align: center;
color: #94a3b8;
}
</style>
</head>
<body>
<div class="header">
<div class="brand-name">RespireX Medical AI</div>
<div class="report-meta">Report ID: #{{ result_id }} | Date: {{ date }}</div>
</div>
<div class="section-label">Patient Demographics</div>
<table class="info-table">
<tr>
<td width="30%"><div class="info-label">Patient Name</div><div class="info-value">{{ patient.full_name|default:patient.user.email }}</div></td>
<td width="20%"><div class="info-label">Age / Gender</div><div class="info-value">{{ patient.age|default:"--" }} / {{ patient.gender|default:"--" }}</div></td>
<td width="20%"><div class="info-label">Patient ID</div><div class="info-value">{{ patient.id }}</div></td>
<td width="30%"><div class="info-label">Location</div><div class="info-value">{{ patient.city|default:"--" }}</div></td>
</tr>
</table>
<div class="section-label">Diagnostic Assessment</div>
<div class="result-container">
<div class="result-main">{{ status_text }}</div>
<div class="result-sub">Risk Classification: <strong>{{ risk_level }}</strong> | Confidence: <strong>{{ mean_score|floatformat:1 }}%</strong></div>
</div>
<div class="section-label">Radiographic & Comparative Analysis</div>
<table class="visual-table">
<tr>
<td>
<div style="font-size: 9px; color: #64748b; margin-bottom: 5px; font-weight: bold;">ANALYZED RADIOGRAPH</div>
<div class="image-box">
{% if xray_img %}
<img src="{{ xray_img }}" style="height: 100%; width: auto; object-fit: contain;">
{% else %}
<div style="padding-top: 80px; color: #cbd5e1;">Image Not Available</div>
{% endif %}
</div>
</td>
<td>
<div style="font-size: 9px; color: #64748b; margin-bottom: 5px; font-weight: bold;">POPULATION RISK ANALYSIS</div>
<div class="chart-box">
{% if scatter_plot %}
<img src="{{ scatter_plot }}" style="width: 100%; height: auto;">
{% else %}
<div style="padding-top: 80px; color: #cbd5e1;">Chart Generation Failed</div>
{% endif %}
</div>
</td>
</tr>
</table>
<div class="section-label">Detailed Metrics</div>
<table class="metrics-table">
<tr>
<th width="40%">Analysis Metric</th>
<th width="20%">Score</th>
<th width="40%">Clinical Significance</th>
</tr>
<tr>
<td>AI Model Prediction</td>
<td>{{ model_conf|floatformat:1 }}%</td>
<td>Computer-Aided Detection (CAD) Score</td>
</tr>
<tr>
<td>Symptom Correlation</td>
<td>{{ symptom_score|floatformat:1 }}%</td>
<td>Self-reported symptom severity index</td>
</tr>
<tr>
<td><strong>Composite Score</strong></td>
<td class="score-highlight">{{ mean_score|floatformat:1 }}%</td>
<td>Weighted diagnostic probability</td>
</tr>
</table>
<div class="section-label">{{ meds_title }}</div>
<div style="font-size: 8px; color: #64748b; font-style: italic; margin-bottom: 4px;">Note: {{ meds_note }}</div>
<table class="meds-table">
<thead>
<tr>
<th width="40%">Medication</th>
<th width="20%">Dosage</th>
<th width="40%">Indication</th>
</tr>
</thead>
<tbody>
{% for med in meds_list %}
<tr>
<td><strong>{{ med.name }}</strong></td>
<td><span class="dose-badge">{{ med.dose }}</span></td>
<td style="color: #64748b;">{{ med.desc }}</td>
</tr>
{% endfor %}
</tbody>
</table>
<div class="disclaimer-box">
<strong>DISCLAIMER:</strong> This report is generated by the RespireX Artificial Intelligence system.<br/>
It is intended for screening purposes only and DOES NOT constitute a final medical diagnosis.
</div>
<div id="footerContent">
© 2025 RespireX. All rights reserved.<br/>
By Team BitBash
</div>
</body>
</html>
"""
# --- 6. Render ---
template = Template(html_string)
context = Context({
'theme_color': theme_color,
'accent_color': accent_color,
'light_bg': light_bg,
'status_text': status_text,
'patient': test_result.patient,
'result_id': test_result.id,
'date': test_result.date_tested.strftime('%B %d, %Y'),
'risk_level': current_risk_level, # <--- Using the Recalculated Variable
'model_conf': model_conf,
'symptom_score': symptom_score,
'mean_score': mean_score,
'meds_title': meds_title,
'meds_note': meds_note,
'meds_list': meds_list,
'xray_img': xray_img_b64,
'scatter_plot': scatter_plot_b64
})
html = template.render(context)
result = io.BytesIO()
pisa_status = pisa.CreatePDF(io.BytesIO(html.encode("UTF-8")), dest=result)
if pisa_status.err:
raise Exception("PDF Generation Error")
result.seek(0)
return result |