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Sleeping
| import os # Import the os module | |
| 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", "🔻", "#fff3cd") | |
| elif value > high: | |
| return ("High", "🔺", "#f8d7da") | |
| else: | |
| return ("Normal", "✅", "#d4edda") | |
| # Function to build table for test results | |
| def build_table(title, rows): | |
| html = ( | |
| f'<div style="margin-bottom: 25px; border-radius: 8px; overflow: hidden; border: 1px solid #e0e0e0;">' | |
| f'<div style="background: linear-gradient(135deg, #f5f7fa, #c3cfe2); padding: 12px 16px; border-bottom: 1px solid #e0e0e0;">' | |
| f'<h4 style="margin: 0; color: #2c3e50; font-size: 16px; font-weight: 600;">{title}</h4>' | |
| f'</div>' | |
| f'<table style="width:100%; border-collapse:collapse; background: white;">' | |
| f'<thead><tr style="background:#f8f9fa;"><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:left;font-size:13px;">Test</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Result</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Range</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Level</th></tr></thead><tbody>' | |
| ) | |
| 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'<tr style="background:{row_bg};border-bottom:1px solid #e9ecef;"><td style="padding:10px 8px;color:#2c3e50;font-weight:500;">{label}</td><td style="padding:10px 8px;text-align:center;color:#2c3e50;font-weight:600;">{value_str}</td><td style="padding:10px 8px;text-align:center;color:#6c757d;font-size:12px;">{ref[0]} - {ref[1]}</td><td style="padding:10px 8px;text-align:center;font-weight:600;color:{"#28a745" if level == "Normal" else "#dc3545" if level == "High" else "#ffc107"};">{icon} {level}</td></tr>' | |
| html += '</tbody></table></div>' | |
| return html | |
| # Function to save the health report to PDF (showing only the Health Card section) | |
| 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)) | |
| flowables.append(Spacer(1, 12)) | |
| # Only add the health card content here | |
| for label, value in test_results.items(): | |
| flowables.append(Paragraph(value, 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 | |
| # Function for Health Card Output (removes patient details) | |
| def build_health_card(profile_image, test_results, summary): | |
| from datetime import datetime | |
| current_date = datetime.now().strftime("%B %d, %Y") | |
| # Build the Health Card HTML | |
| html = f""" | |
| <div id="health-card" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 700px; margin: 20px auto; border-radius: 16px; background: linear-gradient(135deg, #e3f2fd 0%, #f3e5f5 100%); border: 2px solid #ddd; box-shadow: 0 8px 32px rgba(0, 0, 0, 0.15); padding: 30px; color: #1a1a1a;"> | |
| <div style="background-color: rgba(255, 255, 255, 0.9); border-radius: 12px; padding: 20px; margin-bottom: 25px; border: 1px solid #e0e0e0;"> | |
| <div style="display: flex; align-items: center; margin-bottom: 15px;"> | |
| <div style="background: linear-gradient(135deg, #64b5f6, #42a5f5); padding: 8px 16px; border-radius: 8px; margin-right: 20px;"> | |
| <h3 style="margin: 0; font-size: 16px; color: white; font-weight: 600;">HEALTH CARD</h3> | |
| </div> | |
| <div style="margin-left: auto; text-align: right; color: #666; font-size: 12px;"> | |
| <div>Report Date: {current_date}</div> | |
| </div> | |
| </div> | |
| <div style="display: flex; align-items: center;"> | |
| <img src="data:image/png;base64,{profile_image}" alt="Profile" style="width: 90px; height: 90px; border-radius: 50%; margin-right: 20px; border: 3px solid #fff; box-shadow: 0 4px 12px rgba(0,0,0,0.1);"> | |
| <div> | |
| <h2 style="margin: 0; font-size: 28px; color: #2c3e50; font-weight: 700;">Face-Based Health Analysis Report</h2> | |
| </div> | |
| </div> | |
| </div> | |
| <div style="background-color: rgba(255, 255, 255, 0.95); border-radius: 12px; padding: 25px; margin-bottom: 25px; border: 1px solid #e0e0e0;"> | |
| {test_results} | |
| </div> | |
| <div style="background-color: rgba(255, 255, 255, 0.95); padding: 20px; border-radius: 12px; border: 1px solid #e0e0e0; margin-bottom: 25px;"> | |
| <h4 style="margin: 0 0 15px 0; color: #2c3e50; font-size: 18px; font-weight: 600;">📝 Summary & Recommendations</h4> | |
| <div style="color: #444; line-height: 1.6;"> | |
| {summary} | |
| </div> | |
| </div> | |
| <div style="display: flex; gap: 15px; justify-content: center; flex-wrap: wrap;"> | |
| <button onclick="window.print()" style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;"> | |
| 📥 Download Report | |
| </button> | |
| </div> | |
| </div> | |
| """ | |
| return html | |
| # Define the Gradio interface | |
| def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id): | |
| if mode == "Image" and image is None: | |
| return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None | |
| if mode == "Video" and video is None: | |
| return "<div style='color:red;'>⚠️ Error: No video provided.</div>", None | |
| # Process the inputs for analysis | |
| 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("""# 🧠 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="📸 Upload Face Image") | |
| video_input = gr.Video(label="Upload Face Video", sources=["upload", "webcam"]) | |
| submit_btn = gr.Button("🔍 Analyze") | |
| with gr.Column(): | |
| result_html = gr.HTML(label="🧪 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 interface | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |