Update app.py

app.py

@@ -6,9 +6,9 @@ from sklearn.linear_model import LinearRegression
 import random
 import base64
 import joblib
-import pdfkit
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
 from io import BytesIO
-import json
 
 # Initialize the face mesh model
 mp_face_mesh = mp.solutions.face_mesh
@@ -117,15 +117,23 @@ def build_table(title, rows):
     return html
 
 
-# Generate PDF from HTML content
+# Generate PDF from HTML content using reportlab
 def generate_pdf(html_content):
-    pdf = pdfkit.from_string(html_content, False)
-    return BytesIO(pdf)
+    buffer = BytesIO()
+    c = canvas.Canvas(buffer, pagesize=letter)
+    
+    # Adding basic content to PDF (you can modify this to match your layout)
+    text = c.beginText(40, 750)
+    text.setFont("Helvetica", 12)
+    text.textLines(html_content)  # Add the content
+
+    c.drawText(text)
+    c.showPage()
+    c.save()
+
+    buffer.seek(0)
+    return buffer
 
-# Find labs using geolocation (for example purposes, replace with actual logic)
-def find_labs_nearby(location):
-    # This can use the location to query a database of nearby labs
-    return f"Nearby labs for your location {location}: Lab A, Lab B, Lab C."
 
 # Modified analyze_face function
 def analyze_face(input_data):
@@ -278,7 +286,6 @@ with gr.Blocks() as demo:
             result_html = gr.HTML(label="🧪 Health Report Table")
             result_image = gr.Image(label="📷 Key Frame Snapshot")
             download_btn = gr.Button("📥 Download PDF")
-            download_btn.click(fn=lambda pdf_file: pdf_file, inputs=[result_html], outputs=[download_btn])
             
     submit_btn.click(fn=route_inputs,
                      inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],