Update app.py

app.py

@@ -1,14 +1,11 @@
 import gradio as gr
 from PIL import Image, ImageDraw
-import os
 import sys
+import os
 import time
 import threading
 import logging
 
-# Add the backend app to the system path for imports
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
-
 # Set up logging configuration
 logging.basicConfig(level=logging.INFO)
 
@@ -18,16 +15,13 @@ from llama import generate_response_based_on_yolo
 
 # Define model path and output folder relative to the app's directory
 MODEL_PATH = 'yolov8n_custom.pkl'  # Update this to the relative path of your model on Hugging Face Spaces
-OUTPUT_FOLDER = './output_images'  # Use a relative path
+OUTPUT_FOLDER = 'output_images'  # Use a relative path to avoid permission issues
 
 # Ensure the output folder exists
 os.makedirs(OUTPUT_FOLDER, exist_ok=True)
 
-# Initialize the fracture detector
-try:
-    detector = FractureDetector(MODEL_PATH, OUTPUT_FOLDER)
-except Exception as e:
-    logging.error(f"Failed to initialize FractureDetector: {str(e)}")
+# Initialize the fracture detector with both parameters
+detector = FractureDetector(MODEL_PATH, OUTPUT_FOLDER)
 
 def delete_file_after_delay(file_path, delay):
     """Delete the specified file after a given delay."""
@@ -52,18 +46,32 @@ def mark_fracture_area(image, detections):
 
 def analyze_image(input_image):
     """Analyze the uploaded image for fractures and generate a report."""
+    logging.info("Starting analysis on uploaded image.")
+
+    # Save the uploaded image to a temporary location
     temp_image_path = os.path.join(OUTPUT_FOLDER, 'temp_uploaded_image.jpg')
     input_image.save(temp_image_path)
 
     try:
+        logging.info("Performing fracture detection.")
+        # Perform fracture detection
         detections = detector.detect_fractures(temp_image_path, conf_threshold=0.25)
+
+        # Mark the fracture areas on the image
         marked_image = mark_fracture_area(input_image.copy(), detections)
+
+        # Generate analysis report
         analysis_report = generate_response_based_on_yolo(detections)
+
+        # Schedule deletion of the temporary file after 2 minutes
         delete_file_after_delay(temp_image_path, 120)
+
+        logging.info("Analysis completed successfully.")
         return marked_image, analysis_report
+
     except Exception as e:
         logging.error(f"An error occurred during analysis: {str(e)}")
-        return input_image, f"An error occurred: {str(e)}"
+        return input_image, f"An error occurred during analysis: {str(e)}"
 
 # Define the Gradio interface
 iface = gr.Interface(