Update app.py

app.py

@@ -10,7 +10,7 @@ import joblib
 from datetime import datetime
 import shutil
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib import colors
 
@@ -33,7 +33,6 @@ def extract_features(image, landmarks):
 
     return [red_percent, green_percent, blue_percent]
 
-
 def train_model(output_range):
     X = [[
         random.uniform(0.2, 0.5),
@@ -55,8 +54,7 @@ try:
     spo2_model = joblib.load("spo2_model_simulated.pkl")
     hr_model = joblib.load("heart_rate_model.pkl")
 except FileNotFoundError:
-    print(
-        "Error: One or more .pkl model files are missing. Please upload them.")
+    print("Error: One or more .pkl model files are missing. Please upload them.")
     exit(1)
 
 models = {
@@ -81,7 +79,6 @@ models = {
     "Temperature": train_model((97, 99))
 }
 
-
 # Helper function for risk level color coding
 def get_risk_color(value, normal_range):
     low, high = normal_range
@@ -92,7 +89,6 @@ def get_risk_color(value, normal_range):
     else:
         return ("Normal", "✅", "#d4edda")
 
-
 # Function to build table for test results
 def build_table(title, rows):
     html = (
@@ -119,7 +115,6 @@ def build_table(title, rows):
     html += '</tbody></table></div>'
     return html
 
-
 # Function to save the health report to PDF
 def save_results_to_pdf(test_results, filename):
     try:
@@ -164,7 +159,6 @@ def save_results_to_pdf(test_results, filename):
     except Exception as e:
         return f"Error saving PDF: {str(e)}", None
 
-
 # Build health card layout
 def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id=""):
     from datetime import datetime
@@ -220,7 +214,6 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
     """
     return html
 
-
 # Initialize global variable for patient details
 current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}
 
@@ -245,8 +238,7 @@ def analyze_face(input_data):
     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  # Fixed: Use integer index
+    landmarks = result.multi_face_landmarks[0].landmark  # Fixed: Use integer index
     features = extract_features(frame_rgb, landmarks)
     test_values = {}
     r2_scores = {}
@@ -257,8 +249,7 @@ def analyze_face(input_data):
             test_values[label] = prediction
             r2_scores[label] = 0.385
         else:
-            value = models[label].predict(
-                [[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
+            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
             test_values[label] = value
             r2_scores[label] = 0.0