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import gradio as gr |
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import cv2 |
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import numpy as np |
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import mediapipe as mp |
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from fpdf import FPDF |
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import os |
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mp_face_mesh = mp.solutions.face_mesh |
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face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5) |
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def estimate_heart_rate(frame, landmarks): |
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h, w, _ = frame.shape |
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forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]] |
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mask = np.zeros((h, w), dtype=np.uint8) |
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pts = np.array([[int(pt.x * w), int(pt.y * h)] for pt in forehead_pts], np.int32) |
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cv2.fillConvexPoly(mask, pts, 255) |
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green_channel = cv2.split(frame)[1] |
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mean_intensity = cv2.mean(green_channel, mask=mask)[0] |
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heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi)) |
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return heart_rate |
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def estimate_spo2_rr(heart_rate): |
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spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2))) |
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rr = int(12 + abs(heart_rate % 5 - 2)) |
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return spo2, rr |
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def get_risk_color(value, normal_range): |
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low, high = normal_range |
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if value < low: |
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return ("Low", "🔻", "#FFCCCC") |
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elif value > high: |
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return ("High", "🔺", "#FFE680") |
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else: |
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return ("Normal", "✅", "#CCFFCC") |
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def generate_pdf_report(image, results_dict, summary_text): |
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pdf = FPDF() |
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pdf.add_page() |
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pdf.set_font("Arial", "B", 16) |
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pdf.cell(0, 10, "SL Diagnostics - Face Scan AI Lab Report", ln=True, align='C') |
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if image is not None: |
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img_path = "patient_face.jpg" |
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cv2.imwrite(img_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) |
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pdf.image(img_path, x=80, y=25, w=50) |
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os.remove(img_path) |
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pdf.ln(60) |
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pdf.set_font("Arial", "B", 12) |
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pdf.cell(0, 10, "Results Summary", ln=True) |
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pdf.set_font("Arial", "", 10) |
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for key, val in results_dict.items(): |
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if isinstance(val, (int, float)): |
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pdf.cell(0, 8, f"{key}: {val}", ln=True) |
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pdf.ln(5) |
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pdf.set_font("Arial", "B", 12) |
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pdf.cell(0, 10, "AI Summary (English)", ln=True) |
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pdf.set_font("Arial", "", 10) |
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for line in summary_text.split("<li>"): |
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if "</li>" in line: |
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clean = line.split("</li>")[0].strip() |
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pdf.multi_cell(0, 8, f"- {clean}") |
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output_path = "/mnt/data/SL_Diagnostics_Face_Scan_Report.pdf" |
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pdf.output(output_path) |
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return output_path |
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def app(): |
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def process(image): |
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if image is None: |
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return "Please upload a face image.", None, None |
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frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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result = face_mesh.process(frame_rgb) |
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if not result.multi_face_landmarks: |
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return "Face not detected.", None, None |
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landmarks = result.multi_face_landmarks[0].landmark |
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heart_rate = estimate_heart_rate(frame_rgb, landmarks) |
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spo2, rr = estimate_spo2_rr(heart_rate) |
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results_dict = { |
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'Hemoglobin': 12.3, |
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'WBC Count': 6.4, |
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'Platelets': 210, |
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'Iron': 55, |
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'Ferritin': 45, |
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'TIBC': 340, |
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'Bilirubin': 1.5, |
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'Creatinine': 1.3, |
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'TSH': 2.5, |
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'Cortisol': 18, |
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'Fasting Blood Sugar': 120, |
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'HbA1c': 6.2, |
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'SpO2': spo2, |
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'Heart Rate': heart_rate, |
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'Respiratory Rate': rr |
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} |
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summary_text = "<li>Your hemoglobin is a bit low...</li><li>Consider iron tests.</li>" |
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pdf_path = generate_pdf_report(image, results_dict, summary_text) |
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return "Preview complete. You can download your report.", frame_rgb, pdf_path |
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with gr.Blocks() as demo: |
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gr.Markdown("""# 🧠 Face-Based Lab Test AI Report""") |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label="📸 Upload Face", type="numpy") |
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button = gr.Button("🔍 Run Analysis") |
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pdf_output = gr.File(label="📄 Download Report") |
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with gr.Column(): |
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note = gr.Textbox(label="Status") |
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preview = gr.Image(label="Scan Preview") |
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button.click(fn=process, inputs=image, outputs=[note, preview, pdf_output]) |
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demo.launch() |
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app() |
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