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
# 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):
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
# Format the value with appropriate units
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, filename):
try:
# Create a PDF document
doc = SimpleDocTemplate(filename, pagesize=letter)
styles = getSampleStyleSheet()
# Define custom styles
title_style = ParagraphStyle(
name='Title',
fontSize=16,
leading=20,
alignment=1, # Center
spaceAfter=20,
textColor=colors.black,
fontName='Helvetica-Bold'
)
body_style = ParagraphStyle(
name='Body',
fontSize=12,
leading=14,
spaceAfter=10,
textColor=colors.black,
fontName='Helvetica'
)
# Build the PDF content
flowables = []
# Add title
flowables.append(Paragraph("Health Report", title_style))
# Add test results to the report
for label, value in test_results.items():
line = f"{label}: {value}"
flowables.append(Paragraph(line, body_style))
flowables.append(Spacer(1, 12))
# Build the PDF
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, patient_name="", patient_age="", patient_gender="", patient_id=""):
from datetime import datetime
current_date = datetime.now().strftime("%B %d, %Y")
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)) # Adjust resolution for Replit
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result = face_mesh.process(frame_rgb)
if not result.multi_face_landmarks:
return "
Your hemoglobin is a bit low โ this could mean mild anemia.
Low iron storage detected โ consider an iron profile test.
Elevated bilirubin โ possible jaundice. Recommend LFT.
High HbA1c โ prediabetes indication. Recommend glucose check.
Low SpOโ โ suggest retesting with a pulse oximeter.
"
_, buffer = cv2.imencode('.png', frame_rgb)
profile_image_base64 = base64.b64encode(buffer).decode('utf-8')
# Use global patient details
global current_patient_details
health_card_html = build_health_card(
profile_image_base64,
test_results,
summary,
current_patient_details['name'],
current_patient_details['age'],
current_patient_details['gender'],
current_patient_details['id']
)
# Generate PDF and return for download
pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
pdf_result, pdf_filepath = save_results_to_pdf(test_results, pdf_filename)
if pdf_filepath:
# Copy the PDF to a temporary directory for Gradio to serve it
temp_pdf_path = "/tmp/" + os.path.basename(pdf_filepath)
shutil.copy(pdf_filepath, temp_pdf_path)
return health_card_html, temp_pdf_path
# Modified route_inputs function
def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id):
if mode == "Image" and image is None:
return "
โ ๏ธ Error: No image provided.
", None
if mode == "Video" and video is None:
return "
โ ๏ธ Error: No video provided.
", None
# Store patient details globally for use in analyze_face
global current_patient_details
current_patient_details = {
'name': patient_name,
'age': patient_age,
'gender': patient_gender,
'id': patient_id
}
health_card_html, pdf_file_path = analyze_face(image if mode == "Image" else video)
return health_card_html, pdf_file_path
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("""# ๐ง Face-Based Lab Test AI Report (Video Mode)""")
with gr.Row():
with gr.Column():
gr.Markdown("### Patient Information")
patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
patient_age = gr.Number(label="Age", value=25, minimum=1, maximum=120)
patient_gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"], value="Male")
patient_id = gr.Textbox(label="Patient ID", placeholder="Enter patient ID (optional)")
gr.Markdown("### Image/Video Input")
mode_selector = gr.Radio(label="Choose Input Mode",
choices=["Image", "Video"],
value="Image")
image_input = gr.Image(type="numpy", label="๐ธ Upload Face Image")
video_input = gr.Video(label="Upload Face Video",
sources=["upload", "webcam"])
submit_btn = gr.Button("๐ Analyze")
with gr.Column():
result_html = gr.HTML(label="๐งช Health Report Table")
result_pdf = gr.File(label="Download Health Report PDF", interactive=False)
submit_btn.click(fn=route_inputs,
inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
outputs=[result_html, result_pdf])
# Launch Gradio for Replit
demo.launch(server_name="0.0.0.0", server_port=7860)
