Update app.py

app.py

@@ -6,7 +6,12 @@ from sklearn.linear_model import LinearRegression
 import random
 import base64
 import joblib
-from fpdf import FPDF
+import shutil
+from datetime import datetime
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib import colors
 
 # Initialize the face mesh model
 mp_face_mesh = mp.solutions.face_mesh
@@ -15,6 +20,7 @@ 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]
@@ -27,6 +33,7 @@ def extract_features(image, landmarks):
 
     return [red_percent, green_percent, blue_percent]
 
+
 def train_model(output_range):
     X = [[
         random.uniform(0.2, 0.5),
@@ -113,34 +120,49 @@ def build_table(title, rows):
     return html
 
 
-# Generate PDF report using FPDF with support for UTF-8 characters (like emojis)
-def generate_pdf(report_html):
-    pdf = FPDF()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    
-    # Add a page to the PDF
-    pdf.add_page()
-
-    # Add a custom font (download a TTF font that supports Unicode characters and emojis)
-    # Here, 'Arial Unicode MS' is used as an example, but you can use any font supporting emojis
-    # Make sure to have the TTF file in your project and pass it as an argument to add_font
+# Function to save the health report to PDF
+def save_results_to_pdf(test_results, filename):
     try:
-        pdf.add_font('ArialUnicode', '', '/path/to/ArialUnicodeMS.ttf', uni=True)
-        pdf.set_font('ArialUnicode', '', 12)
+        # Create a PDF document
+        doc = SimpleDocTemplate(filename, pagesize=letter)
+        styles = getSampleStyleSheet()
+        
+        # Define custom styles
+        title_style = ParagraphStyle(
+            name='Title',
+            fontSize=16,
+            leading=20,
+            alignment=1,  # Center
+            spaceAfter=20,
+            textColor=colors.black,
+            fontName='Helvetica-Bold'
+        )
+        body_style = ParagraphStyle(
+            name='Body',
+            fontSize=12,
+            leading=14,
+            spaceAfter=10,
+            textColor=colors.black,
+            fontName='Helvetica'
+        )
+        
+        # Build the PDF content
+        flowables = []
+        
+        # Add title
+        flowables.append(Paragraph("Health Report", title_style))
+        
+        # Add test results to the report
+        for label, value in test_results.items():
+            line = f"{label}: {value}"
+            flowables.append(Paragraph(line, body_style))
+            flowables.append(Spacer(1, 12))
+        
+        # Build the PDF
+        doc.build(flowables)
+        return f"PDF saved successfully as {filename}", filename
     except Exception as e:
-        print(f"Error loading font: {e}")
-        pdf.set_font('Arial', '', 12)  # Fallback to a default font if custom font fails
-
-    # Add a title
-    pdf.cell(200, 10, txt="Face-Based Health Report", ln=True, align="C")
-
-    # Write the report HTML content into the PDF
-    pdf.multi_cell(0, 10, txt=report_html)
-
-    # Save the PDF to a file
-    pdf_output = "/mnt/data/health_report.pdf"
-    pdf.output(pdf_output)
-    return pdf_output
+        return f"Error saving PDF: {str(e)}", None
 
 
 # Build health card layout
@@ -150,6 +172,7 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
     
     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;">
@@ -184,6 +207,15 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
                 {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>
     """
     return html
@@ -210,6 +242,7 @@ def analyze_face(input_data):
     # Resize image to reduce processing time
     frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    # Provide image dimensions to mediapipe to avoid NORM_RECT warning
     result = face_mesh.process(frame_rgb)
     if not result.multi_face_landmarks:
         return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
@@ -292,10 +325,17 @@ def analyze_face(input_data):
         current_patient_details['gender'],
         current_patient_details['id']
     )
-
-    # Generate PDF
-    pdf_file_path = generate_pdf(health_card_html)
-    return pdf_file_path
+    
+    # Generate PDF and return for download
+    pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
+    pdf_result, pdf_filepath = save_results_to_pdf(test_results, pdf_filename)
+    
+    if pdf_filepath:
+        # Copy the PDF to a temporary directory for Gradio to serve it
+        temp_pdf_path = "/tmp/" + os.path.basename(pdf_filepath)
+        shutil.copy(pdf_filepath, temp_pdf_path)
+        
+        return health_card_html, temp_pdf_path
 
 
 # Modified route_inputs function
@@ -314,8 +354,8 @@ def route_inputs(mode, image, video, patient_name, patient_age, patient_gender,
         'id': patient_id
     }
     
-    pdf_file_path = analyze_face(image if mode == "Image" else video)
-    return pdf_file_path
+    health_card_html, pdf_file_path = analyze_face(image if mode == "Image" else video)
+    return health_card_html, pdf_file_path
 
 
 # Create Gradio interface
@@ -338,11 +378,13 @@ with gr.Blocks() as demo:
                                    sources=["upload", "webcam"])
             submit_btn = gr.Button("🔍 Analyze")
         with gr.Column():
-            pdf_file = gr.File(label="Download Report (PDF)")
+            result_html = gr.HTML(label="🧪 Health Report Table")
+            result_image = gr.Image(label="📷 Key Frame Snapshot")
+            result_pdf = gr.File(label="Download Health Report PDF")
 
     submit_btn.click(fn=route_inputs,
                      inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
-                     outputs=[pdf_file])
+                     outputs=[result_html, result_pdf])
 
 # Launch Gradio for Replit
 demo.launch(server_name="0.0.0.0", server_port=7860)