Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import cv2
 import numpy as np
@@ -7,6 +6,7 @@ from sklearn.linear_model import LinearRegression
 import random
 import base64
 import joblib
+from fpdf import FPDF
 
 # Initialize the face mesh model
 mp_face_mesh = mp.solutions.face_mesh
@@ -15,7 +15,6 @@ 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]
@@ -28,7 +27,6 @@ def extract_features(image, landmarks):
 
     return [red_percent, green_percent, blue_percent]
 
-
 def train_model(output_range):
     X = [[
         random.uniform(0.2, 0.5),
@@ -115,6 +113,25 @@ def build_table(title, rows):
     return html
 
 
+# Generate PDF report using FPDF
+def generate_pdf(report_html):
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+
+    # 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
+
+
 # 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
@@ -122,7 +139,6 @@ 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;">
@@ -157,40 +173,7 @@ 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>
-    
-    <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
 
@@ -216,7 +199,6 @@ 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
@@ -299,7 +281,10 @@ def analyze_face(input_data):
         current_patient_details['gender'],
         current_patient_details['id']
     )
-    return health_card_html, frame_rgb
+
+    # Generate PDF
+    pdf_file_path = generate_pdf(health_card_html)
+    return pdf_file_path
 
 
 # Modified route_inputs function
@@ -318,8 +303,8 @@ def route_inputs(mode, image, video, patient_name, patient_age, patient_gender,
         'id': patient_id
     }
     
-    health_card_html, frame_rgb = analyze_face(image if mode == "Image" else video)
-    return health_card_html, frame_rgb
+    pdf_file_path = analyze_face(image if mode == "Image" else video)
+    return pdf_file_path
 
 
 # Create Gradio interface
@@ -342,12 +327,12 @@ with gr.Blocks() as demo:
                                    sources=["upload", "webcam"])
             submit_btn = gr.Button("🔍 Analyze")
         with gr.Column():
-            result_html = gr.HTML(label="🧪 Health Report Table")
-            result_image = gr.Image(label="📷 Key Frame Snapshot")
+            download_btn = gr.Button("Download Report (PDF)")
+            download_btn.download(pdf_file_path, "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=[result_html, result_image])
+                     outputs=[download_btn])
 
 # Launch Gradio for Replit
-demo.launch(server_name="0.0.0.0", server_port=7860) 
+demo.launch(server_name="0.0.0.0", server_port=7860)