Update app.py

app.py

@@ -6,6 +6,7 @@ from sklearn.linear_model import LinearRegression
 import random
 import base64
 import joblib
+import pandas as pd
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from io import BytesIO
@@ -17,7 +18,6 @@ face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True,
                                   refine_landmarks=True,
                                   min_detection_confidence=0.5)
 
-
 # Functions for feature extraction
 def extract_features(image, landmarks):
     red_channel = image[:, :, 2]
@@ -117,7 +117,87 @@ def build_table(title, rows):
     return html
 
 
-# Generate PDF from HTML content using reportlab
+# 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
+    current_date = datetime.now().strftime("%B %d, %Y")
+    
+    html = f"""
+    <div id="health-card" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 700px; margin: 20px auto; border-radius: 16px; background: linear-gradient(135deg, #e3f2fd 0%, #f3e5f5 100%); border: 2px solid #ddd; box-shadow: 0 8px 32px rgba(0, 0, 0, 0.15); padding: 30px; color: #1a1a1a;">
+        
+        <div style="background-color: rgba(255, 255, 255, 0.9); border-radius: 12px; padding: 20px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
+            <div style="display: flex; align-items: center; margin-bottom: 15px;">
+                <div style="background: linear-gradient(135deg, #64b5f6, #42a5f5); padding: 8px 16px; border-radius: 8px; margin-right: 20px;">
+                    <h3 style="margin: 0; font-size: 16px; color: white; font-weight: 600;">HEALTH CARD</h3>
+                </div>
+                <div style="margin-left: auto; text-align: right; color: #666; font-size: 12px;">
+                    <div>Report Date: {current_date}</div>
+                    {f'<div>Patient ID: {patient_id}</div>' if patient_id else ''}
+                </div>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <img src="data:image/png;base64,{profile_image}" alt="Profile" style="width: 90px; height: 90px; border-radius: 50%; margin-right: 20px; border: 3px solid #fff; box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
+                <div>
+                    <h2 style="margin: 0; font-size: 28px; color: #2c3e50; font-weight: 700;">{patient_name if patient_name else "Lab Test Results"}</h2>
+                    <p style="margin: 4px 0 0 0; color: #666; font-size: 14px;">{f"Age: {patient_age} | Gender: {patient_gender}" if patient_age and patient_gender else "AI-Generated Health Analysis"}</p>
+                    <p style="margin: 4px 0 0 0; color: #888; font-size: 12px;">Face-Based Health Analysis Report</p>
+                </div>
+            </div>
+        </div>
+
+        <div style="background-color: rgba(255, 255, 255, 0.95); border-radius: 12px; padding: 25px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
+            {test_results['Hematology']}
+            {test_results['Iron Panel']}
+            {test_results['Liver & Kidney']}
+            {test_results['Electrolytes']}
+            {test_results['Vitals']}
+        </div>
+
+        <div style="background-color: rgba(255, 255, 255, 0.95); padding: 20px; border-radius: 12px; border: 1px solid #e0e0e0; margin-bottom: 25px;">
+            <h4 style="margin: 0 0 15px 0; color: #2c3e50; font-size: 18px; font-weight: 600;">📝 Summary & Recommendations</h4>
+            <div style="color: #444; line-height: 1.6;">
+                {summary}
+            </div>
+        </div>
+
+        <div style="display: flex; gap: 15px; justify-content: center; flex-wrap: wrap;">
+            <button onclick="window.print()" style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;">
+                📥 Download Report
+            </button>
+            <button style="padding: 12px 24px; background: linear-gradient(135deg, #2196f3, #1976d2); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(33, 150, 243, 0.3);">
+                📞 Find Labs Near Me
+            </button>
+        </div>
+    </div>
+    
+    <style>
+    @media print {{
+        body * {{
+            visibility: hidden;
+        }}
+        #health-card, #health-card * {{
+            visibility: visible;
+        }}
+        #health-card {{
+            position: absolute;
+            left: 0;
+            top: 0;
+            width: 100% !important;
+            max-width: none !important;
+            margin: 0 !important;
+            box-shadow: none !important;
+            border: none !important;
+        }}
+        button {{
+            display: none !important;
+        }}
+    }}
+    </style>
+    """
+    return html
+
+
+# Function to generate PDF from HTML content using reportlab
 def generate_pdf(html_content):
     buffer = BytesIO()
     c = canvas.Canvas(buffer, pagesize=letter)
@@ -160,12 +240,16 @@ def analyze_face(input_data):
     landmarks = result.multi_face_landmarks[
         0].landmark  # Fixed: Use integer index
     features = extract_features(frame_rgb, landmarks)
+    
+    # Convert features to pandas DataFrame if the model was trained with column names
+    features_df = pd.DataFrame([features], columns=["feature1", "feature2", "feature3"])
+    
     test_values = {}
     r2_scores = {}
 
     for label in models:
         if label == "Hemoglobin":
-            prediction = models[label].predict([features])[0]
+            prediction = models[label].predict(features_df)[0]
             test_values[label] = prediction
             r2_scores[label] = 0.385
         else: