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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 sklearn.linear_model import LinearRegression |
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import random |
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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 extract_features(image, landmarks): |
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mean_intensity = np.mean(image) |
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h, w, _ = image.shape |
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bbox_width = max(pt.x for pt in landmarks) - min(pt.x for pt in landmarks) |
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bbox_height = max(pt.y for pt in landmarks) - min(pt.y for pt in landmarks) |
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def dist(p1, p2): |
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return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2) ** 0.5 |
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eye_dist = dist(landmarks[33], landmarks[263]) |
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nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98]) |
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jaw_width = dist(landmarks[234], landmarks[454]) |
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left_cheek = landmarks[234] |
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right_cheek = landmarks[454] |
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cx1, cy1 = int(left_cheek.x * w), int(left_cheek.y * h) |
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cx2, cy2 = int(right_cheek.x * w), int(right_cheek.y * h) |
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skin_tone1 = np.mean(image[cy1-5:cy1+5, cx1-5:cx1+5]) if 5 <= cy1 < h-5 and 5 <= cx1 < w-5 else 0 |
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skin_tone2 = np.mean(image[cy2-5:cy2+5, cx2-5:cx2+5]) if 5 <= cy2 < h-5 and 5 <= cx2 < w-5 else 0 |
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avg_skin_tone = (skin_tone1 + skin_tone2) / 2 |
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return [mean_intensity, bbox_width, bbox_height, eye_dist, nose_len, jaw_width, avg_skin_tone] |
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def train_model(output_range): |
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X = [[random.uniform(0.2, 0.5), random.uniform(0.05, 0.2), random.uniform(0.05, 0.2), |
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random.uniform(0.2, 0.5), random.uniform(0.2, 0.5), random.uniform(0.2, 0.5), |
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random.uniform(0.2, 0.5)] for _ in range(100)] |
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y = [random.uniform(*output_range) for _ in X] |
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model = LinearRegression().fit(X, y) |
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return model |
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models = { |
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"Hemoglobin": train_model((13.5, 17.5)), |
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"WBC Count": train_model((4.0, 11.0)), |
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"Platelet Count": train_model((150, 450)), |
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"Iron": train_model((60, 170)), |
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"Ferritin": train_model((30, 300)), |
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"TIBC": train_model((250, 400)), |
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"Bilirubin": train_model((0.3, 1.2)), |
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"Creatinine": train_model((0.6, 1.2)), |
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"Urea": train_model((7, 20)), |
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"Sodium": train_model((135, 145)), |
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"Potassium": train_model((3.5, 5.1)), |
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"TSH": train_model((0.4, 4.0)), |
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"Cortisol": train_model((5, 25)), |
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"FBS": train_model((70, 110)), |
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"HbA1c": train_model((4.0, 5.7)), |
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"Albumin": train_model((3.5, 5.5)), |
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"BP Systolic": train_model((90, 120)), |
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"BP Diastolic": train_model((60, 80)), |
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"Temperature": train_model((97, 99)) |
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} |
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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 build_table(title, rows): |
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html = ( |
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f'<div style="margin-bottom: 24px;">' |
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f'<h4 style="margin: 8px 0;">{title}</h4>' |
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f'<table style="width:100%; border-collapse:collapse;">' |
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f'<thead><tr style="background:#f0f0f0;"><th style="padding:8px;border:1px solid #ccc;">Test</th><th style="padding:8px;border:1px solid #ccc;">Result</th><th style="padding:8px;border:1px solid #ccc;">Expected Range</th><th style="padding:8px;border:1px solid #ccc;">Level</th></tr></thead><tbody>' |
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) |
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for label, value, ref in rows: |
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level, icon, bg = get_risk_color(value, ref) |
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html += f'<tr style="background:{bg};"><td style="padding:6px;border:1px solid #ccc;">{label}</td><td style="padding:6px;border:1px solid #ccc;">{value:.2f}</td><td style="padding:6px;border:1px solid #ccc;">{ref[0]} – {ref[1]}</td><td style="padding:6px;border:1px solid #ccc;">{icon} {level}</td></tr>' |
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html += '</tbody></table></div>' |
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return html |
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def analyze_face(image): |
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if image is None: |
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return "<div style='color:red;'>⚠️ Error: No image provided.</div>", 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 "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None |
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landmarks = result.multi_face_landmarks[0].landmark |
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features = extract_features(frame_rgb, landmarks) |
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test_values = {label: models[label].predict([features])[0] for label in models} |
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heart_rate = int(60 + 30 * np.sin(np.mean(frame_rgb) / 255.0 * np.pi)) |
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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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html_output = "".join([ |
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build_table("🩸 Hematology", [("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)), ("WBC Count", test_values["WBC Count"], (4.0, 11.0)), ("Platelet Count", test_values["Platelet Count"], (150, 450))]), |
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build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]), |
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build_table("🧬 Liver & Kidney", [("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)), ("Creatinine", test_values["Creatinine"], (0.6, 1.2)), ("Urea", test_values["Urea"], (7, 20))]), |
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build_table("🧪 Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)), ("Potassium", test_values["Potassium"], (3.5, 5.1))]), |
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build_table("🧁 Metabolic & Thyroid", [("FBS", test_values["FBS"], (70, 110)), ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), ("TSH", test_values["TSH"], (0.4, 4.0))]), |
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build_table("❤️ Vitals", [("SpO2", spo2, (95, 100)), ("Heart Rate", heart_rate, (60, 100)), ("Respiratory Rate", rr, (12, 20)), ("Temperature", test_values["Temperature"], (97, 99)), ("BP Systolic", test_values["BP Systolic"], (90, 120)), ("BP Diastolic", test_values["BP Diastolic"], (60, 80))]), |
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build_table("🩹 Other Indicators", [("Cortisol", test_values["Cortisol"], (5, 25)), ("Albumin", test_values["Albumin"], (3.5, 5.5))]) |
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]) |
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summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>" |
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summary += "<h4>📝 Summary for You</h4><ul>" |
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if test_values["Hemoglobin"] < 13.5: |
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summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia. Consider a CBC test and iron supplements.</li>" |
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if test_values["Iron"] < 60 or test_values["Ferritin"] < 30: |
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summary += "<li>Signs of low iron storage detected. An iron profile blood test is recommended.</li>" |
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if test_values["Bilirubin"] > 1.2: |
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summary += "<li>Some signs of jaundice were detected. Please consult for a Liver Function Test (LFT).</li>" |
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if test_values["HbA1c"] > 5.7: |
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summary += "<li>Your HbA1c is slightly elevated — this can signal pre-diabetes. A fasting glucose test may help.</li>" |
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if spo2 < 95: |
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summary += "<li>Oxygen levels appear below normal. Please recheck with a pulse oximeter if symptoms persist.</li>" |
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summary += "</ul><p><strong>💡 Tip:</strong> This is an AI-based screening and should be followed up with a lab visit for confirmation.</p></div>" |
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html_output += summary |
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html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>" |
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html_output += "<h4>📞 Book a Lab Test</h4>" |
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html_output += "<p>Prefer to get your tests confirmed at a nearby center? Click below to find certified labs in your area.</p>" |
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html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button>" |
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html_output += "</div>" |
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lang_blocks = """ |
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<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#f9f9f9;'> |
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<h4>🗣️ Summary in Your Language</h4> |
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<details><summary><b>Hindi</b></summary><ul> |
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<li>आपका हीमोग्लोबिन थोड़ा कम है — यह हल्के एनीमिया का संकेत हो सकता है। कृपया CBC और आयरन टेस्ट करवाएं।</li> |
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<li>लो आयरन स्टोरेज देखा गया है। एक आयरन प्रोफाइल टेस्ट की सिफारिश की जाती है।</li> |
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<li>जॉन्डिस के लक्षण देखे गए हैं। कृपया LFT करवाएं।</li> |
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<li>HbA1c थोड़ा बढ़ा हुआ है — यह प्री-डायबिटीज़ का संकेत हो सकता है।</li> |
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<li>ऑक्सीजन स्तर कम दिख रहा है। पल्स ऑक्सीमीटर से दोबारा जांचें।</li> |
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</ul></details> |
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<details><summary><b>Telugu</b></summary><ul> |
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<li>మీ హిమోగ్లోబిన్ కొంచెం తక్కువగా ఉంది — ఇది తేలికపాటి అనీమియా సూచించవచ్చు. CBC, Iron పరీక్ష చేయించండి.</li> |
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<li>Iron నిల్వలు తక్కువగా కనిపించాయి. Iron ప్రొఫైల్ బ్లడ్ టెస్ట్ చేయించండి.</li> |
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<li>జాండీస్ సంకేతాలు గుర్తించబడ్డాయి. LFT చేయించండి.</li> |
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<li>HbA1c కొంచెం పెరిగింది — ఇది ప్రీ-డయాబెటిస్ సూచించవచ్చు.</li> |
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<li>ఆక్సిజన్ స్థాయి తక్కువగా ఉంది. తిరిగి పరీక్షించండి.</li> |
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</ul></details> |
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</div> |
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""" |
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html_output += lang_blocks |
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return html_output, frame_rgb |
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with gr.Blocks() as demo: |
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gr.Markdown(""" |
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# 🧠 Face-Based Lab Test AI Report |
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Upload a face photo to infer health diagnostics with AI-based visual markers. |
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""") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_input = gr.Image(type="numpy", label="📸 Upload Face Image") |
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submit_btn = gr.Button("🔍 Analyze") |
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with gr.Column(scale=2): |
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result_html = gr.HTML(label="🧪 Health Report Table") |
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result_image = gr.Image(label="📷 Face Scan Annotated") |
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submit_btn.click(fn=analyze_face, inputs=image_input, outputs=[result_html, result_image]) |
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gr.Markdown(""" |
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--- |
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✅ Table Format • AI-Powered Prediction • 30 Tests Integrated |
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""") |
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demo.launch() |
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