Update app.py

app.py

@@ -32,6 +32,10 @@ def train_model(output_range):
 import joblib
 hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
 
+hemoglobin_r2 = 0.385
+import joblib
+spo2_model = joblib.load("spo2_model_simulated.pkl")
+
 models = {
     "Hemoglobin": hemoglobin_model,
     "WBC Count": train_model((4.0, 11.0)),
@@ -86,15 +90,25 @@ def analyze_face(image):
     landmarks = result.multi_face_landmarks[0].landmark
     features = extract_features(frame_rgb, landmarks)
     test_values = {}
+    r2_scores = {}
     for label in models:
         if label == "Hemoglobin":
-            test_values[label] = models[label].predict([features])[0]  # 3D input
+            prediction = models[label].predict([features])[0]
+            test_values[label] = prediction
+            r2_scores[label] = hemoglobin_r2
         else:
-            test_values[label] = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]  # simulate other 7D inputs
+            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
+            test_values[label] = value
+            r2_scores[label] = 0.0  # simulate other 7D inputs
     heart_rate = int(60 + 30 * np.sin(np.mean(frame_rgb) / 255.0 * np.pi))
-    spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2)))
+    skin_patch = frame_rgb[100:150, 100:150]
+    skin_tone_index = np.mean(skin_patch) / 255 if skin_patch.size else 0.5
+    brightness_variation = np.std(cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)) / 255
+    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
+    spo2 = spo2_model.predict([spo2_features])[0]
     rr = int(12 + abs(heart_rate % 5 - 2))
     html_output = "".join([
+        f'<div style="font-size:14px;color:#888;margin-bottom:10px;">Hemoglobin R² Score: {r2_scores.get("Hemoglobin", "NA"):.2f}</div>',
         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))]),
         build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]),
         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))]),