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| # Face Detection-Based AI Automation of Lab Tests | |
| # UI: Clean table, multilingual summary, PDF-ready | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| import mediapipe as mp | |
| from fpdf import FPDF | |
| import os | |
| 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) | |
| def estimate_heart_rate(frame, landmarks): | |
| h, w, _ = frame.shape | |
| forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]] | |
| mask = np.zeros((h, w), dtype=np.uint8) | |
| pts = np.array([[int(pt.x * w), int(pt.y * h)] for pt in forehead_pts], np.int32) | |
| cv2.fillConvexPoly(mask, pts, 255) | |
| green_channel = cv2.split(frame)[1] | |
| mean_intensity = cv2.mean(green_channel, mask=mask)[0] | |
| heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi)) | |
| return heart_rate | |
| def estimate_spo2_rr(heart_rate): | |
| spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2))) | |
| rr = int(12 + abs(heart_rate % 5 - 2)) | |
| return spo2, rr | |
| def get_risk_color(value, normal_range): | |
| low, high = normal_range | |
| if value < low: | |
| return ("Low", "🔻", "#FFCCCC") | |
| elif value > high: | |
| return ("High", "🔺", "#FFE680") | |
| else: | |
| return ("Normal", "✅", "#CCFFCC") | |
| def generate_pdf_report(image, results_dict, summary_text): | |
| pdf = FPDF() | |
| pdf.add_page() | |
| pdf.set_font("Arial", "B", 16) | |
| pdf.cell(0, 10, "SL Diagnostics - Face Scan AI Lab Report", ln=True, align='C') | |
| if image is not None: | |
| img_path = "patient_face.jpg" | |
| cv2.imwrite(img_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) | |
| pdf.image(img_path, x=80, y=25, w=50) | |
| os.remove(img_path) | |
| pdf.ln(60) | |
| pdf.set_font("Arial", "B", 12) | |
| pdf.cell(0, 10, "Results Summary", ln=True) | |
| pdf.set_font("Arial", "", 10) | |
| for key, val in results_dict.items(): | |
| if isinstance(val, (int, float)): | |
| pdf.cell(0, 8, f"{key}: {val}", ln=True) | |
| pdf.ln(5) | |
| pdf.set_font("Arial", "B", 12) | |
| pdf.cell(0, 10, "AI Summary (English)", ln=True) | |
| pdf.set_font("Arial", "", 10) | |
| for line in summary_text.split("<li>"): | |
| if "</li>" in line: | |
| clean = line.split("</li>")[0].strip() | |
| pdf.multi_cell(0, 8, f"- {clean}") | |
| output_path = "/mnt/data/SL_Diagnostics_Face_Scan_Report.pdf" | |
| pdf.output(output_path) | |
| return output_path | |