Update app.py

app.py

@@ -17,15 +17,13 @@ def extract_features(image, landmarks):
     bbox_width = max(pt.x for pt in landmarks) - min(pt.x for pt in landmarks)
     bbox_height = max(pt.y for pt in landmarks) - min(pt.y for pt in landmarks)
 
-    # Compute facial region ratios (eye distance, nose length, jaw width, etc.)
     def dist(p1, p2):
         return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2) ** 0.5
 
-    eye_dist = dist(landmarks[33], landmarks[263])  # between left and right eye
-    nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98])  # bridge + tip
+    eye_dist = dist(landmarks[33], landmarks[263])
+    nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98])
     jaw_width = dist(landmarks[234], landmarks[454])
 
-    # Skin tone analysis from cheeks
     left_cheek = landmarks[234]
     right_cheek = landmarks[454]
     cx1, cy1 = int(left_cheek.x * w), int(left_cheek.y * h)
@@ -37,12 +35,13 @@ def extract_features(image, landmarks):
     return [mean_intensity, bbox_width, bbox_height, eye_dist, nose_len, jaw_width, avg_skin_tone]
 
 def train_model(output_range):
-    X = [[random.uniform(0.2, 0.5), random.uniform(0.05, 0.2), random.uniform(0.05, 0.2)] for _ in range(100)]
+    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
 
-# Train models for all tests
 models = {
     "Hemoglobin": train_model((13.5, 17.5)),
     "WBC Count": train_model((4.0, 11.0)),
@@ -65,22 +64,6 @@ models = {
     "Temperature": train_model((97, 99))
 }
 
-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:
@@ -102,117 +85,3 @@ def build_table(title, rows):
         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>'
     html += '</tbody></table></div>'
     return html
-
-def analyze_face(image):
-    if image is None:
-        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
-
-    frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(frame_rgb)
-    if not result.multi_face_landmarks:
-        return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
-
-    landmarks = result.multi_face_landmarks[0].landmark
-    heart_rate = estimate_heart_rate(frame_rgb, landmarks)
-    spo2, rr = estimate_spo2_rr(heart_rate)
-
-    features = extract_features(frame_rgb, landmarks)
-    
-    hb = models["Hemoglobin"].predict([features])[0]
-    wbc = models["WBC Count"].predict([features])[0]
-    platelets = models["Platelet Count"].predict([features])[0]
-    iron = models["Iron"].predict([features])[0]
-    ferritin = models["Ferritin"].predict([features])[0]
-    tibc = models["TIBC"].predict([features])[0]
-    bilirubin = models["Bilirubin"].predict([features])[0]
-    creatinine = models["Creatinine"].predict([features])[0]
-    urea = models["Urea"].predict([features])[0]
-    sodium = models["Sodium"].predict([features])[0]
-    potassium = models["Potassium"].predict([features])[0]
-    tsh = models["TSH"].predict([features])[0]
-    cortisol = models["Cortisol"].predict([features])[0]
-    fbs = models["FBS"].predict([features])[0]
-    hba1c = models["HbA1c"].predict([features])[0]
-    albumin = models["Albumin"].predict([features])[0]
-    bp_sys = models["BP Systolic"].predict([features])[0]
-    bp_dia = models["BP Diastolic"].predict([features])[0]
-    temperature = models["Temperature"].predict([features])[0]
-
-    html_output = "".join([
-        build_table("🩸 Hematology", [("Hemoglobin", hb, (13.5, 17.5)), ("WBC Count", wbc, (4.0, 11.0)), ("Platelet Count", platelets, (150, 450))]),
-        build_table("🧬 Iron Panel", [("Iron", iron, (60, 170)), ("Ferritin", ferritin, (30, 300)), ("TIBC", tibc, (250, 400))]),
-        build_table("🧬 Liver & Kidney", [("Bilirubin", bilirubin, (0.3, 1.2)), ("Creatinine", creatinine, (0.6, 1.2)), ("Urea", urea, (7, 20))]),
-        build_table("🧪 Electrolytes", [("Sodium", sodium, (135, 145)), ("Potassium", potassium, (3.5, 5.1))]),
-        build_table("🧁 Metabolic & Thyroid", [("Fasting Blood Sugar", fbs, (70, 110)), ("HbA1c", hba1c, (4.0, 5.7)), ("TSH", tsh, (0.4, 4.0))]),
-        build_table("❤️ Vitals", [("SpO2", spo2, (95, 100)), ("Heart Rate", heart_rate, (60, 100)), ("Respiratory Rate", rr, (12, 20)), ("Temperature", temperature, (97, 99)), ("BP Systolic", bp_sys, (90, 120)), ("BP Diastolic", bp_dia, (60, 80))]),
-        build_table("🩹 Other Indicators", [("Cortisol", cortisol, (5, 25)), ("Albumin", albumin, (3.5, 5.5))])
-    ])
-
-        summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>"
-    summary += "<h4>📝 Summary for You</h4><ul>"
-    if hb < 13.5:
-        summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia. Consider a CBC test and iron supplements.</li>"
-    if iron < 60 or ferritin < 30:
-        summary += "<li>Signs of low iron storage detected. An iron profile blood test is recommended.</li>"
-    if bilirubin > 1.2:
-        summary += "<li>Some signs of jaundice were detected. Please consult for a Liver Function Test (LFT).</li>"
-    if hba1c > 5.7:
-        summary += "<li>Your HbA1c is slightly elevated — this can signal pre-diabetes. A fasting glucose test may help.</li>"
-    if spo2 < 95:
-        summary += "<li>Oxygen levels appear below normal. Please recheck with a pulse oximeter if symptoms persist.</li>"
-    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>"
-
-    html_output += summary
-
-    html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>"
-    html_output += "<h4>📞 Book a Lab Test</h4>"
-    html_output += "<p>Prefer to get your tests confirmed at a nearby center? Click below to find certified labs in your area.</p>"
-    html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button>"
-    html_output += "</div>"
-
-    lang_blocks = """
-    <div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#f9f9f9;'>
-      <h4>🗣️ Summary in Your Language</h4>
-      <details><summary><b>Hindi</b></summary><ul>
-        <li>आपका हीमोग्लोबिन थोड़ा कम है — यह हल्के एनीमिया का संकेत हो सकता है। कृपया CBC और आयरन टेस्ट करवाएं।</li>
-        <li>लो आयरन स्टोरेज देखा गया है। एक आयरन प्रोफाइल टेस्ट की सिफारिश की जाती है।</li>
-        <li>जॉन्डिस के लक्षण देखे गए हैं। कृपया LFT करवाएं।</li>
-        <li>HbA1c थोड़ा बढ़ा हुआ है — यह प्री-डायबिटीज़ का संकेत हो सकता है।</li>
-        <li>ऑक्सीजन स्तर कम दिख रहा है। पल्स ऑक्सीमीटर से दोबारा जांचें।</li>
-      </ul></details>
-
-      <details><summary><b>Telugu</b></summary><ul>
-        <li>మీ హిమోగ్లోబిన్ కొంచెం తక్కువగా ఉంది — ఇది తేలికపాటి అనీమియా సూచించవచ్చు. CBC, Iron పరీక్ష చేయించండి.</li>
-        <li>Iron నిల్వలు తక్కువగా కనిపించాయి. Iron ప్రొఫైల్ బ్లడ్ టెస్ట్ చేయించండి.</li>
-        <li>జాండీస్ సంకేతాలు గుర్తించబడ్డాయి. LFT చేయించండి.</li>
-        <li>HbA1c కొంచెం పెరిగింది — ఇది ప్రీ-డయాబెటిస్ సూచించవచ్చు.</li>
-        <li>ఆక్సిజన్ స్థాయి తక్కువగా ఉంది. తిరిగి పరీక్షించండి.</li>
-      </ul></details>
-    </div>
-    """
-
-    html_output += lang_blocks
-    return html_output, frame_rgb
-
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    # 🧠 Face-Based Lab Test AI Report
-    Upload a face photo to infer health diagnostics with AI-based visual markers.
-    """)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="numpy", label="📸 Upload Face Image")
-            submit_btn = gr.Button("🔍 Analyze")
-        with gr.Column(scale=2):
-            result_html = gr.HTML(label="🧪 Health Report Table")
-            result_image = gr.Image(label="📷 Face Scan Annotated")
-
-    submit_btn.click(fn=analyze_face, inputs=image_input, outputs=[result_html, result_image])
-
-    gr.Markdown("""
-    ---
-    ✅ Table Format • AI-Powered Prediction • 30 Tests Integrated
-    """)
-
-demo.launch()