Update app.py

app.py

@@ -1,3 +1,4 @@
+import os  # Import the os module
 import gradio as gr
 import cv2
 import numpy as np
@@ -6,8 +7,8 @@ from sklearn.linear_model import LinearRegression
 import random
 import base64
 import joblib
-import shutil
 from datetime import datetime
+import shutil
 from reportlab.lib.pagesizes import letter
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
@@ -20,7 +21,6 @@ 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]
@@ -242,7 +242,6 @@ 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
@@ -379,12 +378,11 @@ with gr.Blocks() as demo:
             submit_btn = gr.Button("🔍 Analyze")
         with gr.Column():
             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")
+            result_pdf = gr.File(label="Download Health Report PDF", interactive=False)
 
-    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_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_pdf])
 
 # Launch Gradio for Replit
 demo.launch(server_name="0.0.0.0", server_port=7860)