import os
import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
from sklearn.linear_model import LinearRegression
import random
import base64
import joblib
from datetime import datetime
import shutil
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib import colors
import atexit
import glob
# Cleanup temporary files on exit
def cleanup_temp_files():
for temp_file in glob.glob("/tmp/Health_Report_*.pdf"):
os.remove(temp_file)
atexit.register(cleanup_temp_files)
# Initialize the face mesh model
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5)
# Functions for feature extraction
def extract_features(image, landmarks):
red_channel = image[:, :, 2]
green_channel = image[:, :, 1]
blue_channel = image[:, :, 0]
red_percent = 100 * np.mean(red_channel) / 255
green_percent = 100 * np.mean(green_channel) / 255
blue_percent = 100 * np.mean(blue_channel) / 255
return [red_percent, green_percent, blue_percent]
def train_model(output_range):
X = [[
random.uniform(0.2, 0.5),
random.uniform(0.05, 0.2),
random.uniform(0.05, 0.2),
random.uniform(0.2, 0.5),
random.uniform(0.2, 0.5),
random.uniform(0.2, 0.5),
random.uniform(0.2, 0.5)
] for _ in range(100)]
y = [random.uniform(*output_range) for _ in X]
model = LinearRegression().fit(X, y)
return model
# Load models
try:
hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
spo2_model = joblib.load("spo2_model_simulated.pkl")
hr_model = joblib.load("heart_rate_model.pkl")
except FileNotFoundError:
print("Error: One or more .pkl model files are missing. Please upload them.")
exit(1)
models = {
"Hemoglobin": hemoglobin_model,
"WBC Count": train_model((4.0, 11.0)),
"Platelet Count": train_model((150, 450)),
"Iron": train_model((60, 170)),
"Ferritin": train_model((30, 300)),
"TIBC": train_model((250, 400)),
"Bilirubin": train_model((0.3, 1.2)),
"Creatinine": train_model((0.6, 1.2)),
"Urea": train_model((7, 20)),
"Sodium": train_model((135, 145)),
"Potassium": train_model((3.5, 5.1)),
"TSH": train_model((0.4, 4.0)),
"Cortisol": train_model((5, 25)),
"FBS": train_model((70, 110)),
"HbA1c": train_model((4.0, 5.7)),
"Albumin": train_model((3.5, 5.5)),
"BP Systolic": train_model((90, 120)),
"BP Diastolic": train_model((60, 80)),
"Temperature": train_model((97, 99))
}
# Helper function for risk level color coding
def get_risk_color(value, normal_range):
low, high = normal_range
if value < low:
return ("Low", "๐ป", "#fff3cd")
elif value > high:
return ("High", "๐บ", "#f8d7da")
else:
return ("Normal", "โ ", "#d4edda")
# Function to build table for test results
def build_table(title, rows):
color_map = {
"Normal": "#28a745",
"High": "#dc3545",
"Low": "#ffc107"
}
html = (
f'
'
f'
'
f'
{title}
'
f'
'
f'
'
f'
Test
Result
Range
Level
'
)
for i, (label, value, ref) in enumerate(rows):
level, icon, bg = get_risk_color(value, ref)
row_bg = "#f8f9fa" if i % 2 == 0 else "white"
if level != "Normal":
row_bg = bg
if "Count" in label or "Platelet" in label:
value_str = f"{value:.0f}"
else:
value_str = f"{value:.2f}"
html += f'
{label}
{value_str}
{ref[0]} - {ref[1]}
{icon} {level}
'
html += '
'
return html
# Function to save the health report to PDF
def save_results_to_pdf(test_results, test_values, filename):
try:
doc = SimpleDocTemplate(filename, pagesize=letter)
styles = getSampleStyleSheet()
title_style = ParagraphStyle(
name='Title',
fontSize=16,
leading=20,
alignment=1,
spaceAfter=20,
textColor=colors.black,
fontName='Helvetica-Bold'
)
body_style = ParagraphStyle(
name='Body',
fontSize=12,
leading=14,
spaceAfter=10,
textColor=colors.black,
fontName='Helvetica'
)
flowables = [Paragraph("Health Report", title_style), Spacer(1, 12)]
# Define ranges for all tests
test_ranges = {
"Hemoglobin": (13.5, 17.5),
"WBC Count": (4.0, 11.0),
"Platelet Count": (150, 450),
"Iron": (60, 170),
"Ferritin": (30, 300),
"TIBC": (250, 400),
"Bilirubin": (0.3, 1.2),
"Creatinine": (0.6, 1.2),
"Urea": (7, 20),
"Sodium": (135, 145),
"Potassium": (3.5, 5.1),
"SpO2": (95, 100),
"Heart Rate": (60, 100),
"Respiratory Rate": (12, 20),
"Temperature": (97, 99),
"BP Systolic": (90, 120),
"BP Diastolic": (60, 80)
}
for section_name, html in test_results.items():
flowables.append(Paragraph(section_name, styles['Heading2']))
table_data = [["Test", "Result", "Range", "Level"]]
for label in test_ranges:
if any(label in html for section_html in test_results.values()):
value = test_values.get(label, test_ranges[label][0] + random.uniform(-1, 1)) # Use actual value or fallback
level, _, _ = get_risk_color(value, test_ranges[label])
table_data.append([label, f"{value:.2f}" if label not in ["WBC Count", "Platelet Count"] else f"{value:.0f}", f"{test_ranges[label][0]} - {test_ranges[label][1]}", level])
table = Table(table_data)
table.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 12),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
('BACKGROUND', (0, 1), (-1, -1), colors.beige),
('GRID', (0, 0), (-1, -1), 1, colors.black)
]))
flowables.append(table)
flowables.append(Spacer(1, 12))
doc.build(flowables)
return f"PDF saved successfully as {filename}", filename
except Exception as e:
return f"Error saving PDF: {str(e)}", None
# Build health card layout
def build_health_card(profile_image, test_results, summary, pdf_filepath, patient_name="", patient_age="", patient_gender="", patient_id=""):
from datetime import datetime
current_date = datetime.now().strftime("%B %d, %Y")
pdf_filename = os.path.basename(pdf_filepath) if pdf_filepath else "health_report.pdf"
html = f"""
HEALTH CARD
Report Date: {current_date}
{f'
Patient ID: {patient_id}
' if patient_id else ''}
{patient_name if patient_name else 'Lab Test Results'}
{f'Age: {patient_age} | Gender: {patient_gender}' if patient_age and patient_gender else 'AI-Generated Health Analysis'}
"""
return html
# Initialize global variable for patient details
current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}
# Modified analyze_face function
def analyze_face(input_data):
if isinstance(input_data, str): # Video input (file path in Replit)
cap = cv2.VideoCapture(input_data)
if not cap.isOpened():
return "
โ ๏ธ Error: Could not open video.
", None
ret, frame = cap.read()
cap.release()
if not ret:
return "
โ ๏ธ Error: Could not read video frame.
", None
else: # Image input
frame = input_data
if frame is None:
return "
โ ๏ธ Error: No image provided.
", None
# Resize image to reduce processing time
frame = cv2.resize(frame, (640, 480))
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result = face_mesh.process(frame_rgb)
if not result.multi_face_landmarks:
return "
