Update app.py

app.py

@@ -10,35 +10,15 @@ from simple_salesforce import Salesforce
 from datetime import datetime
 import base64
 import io
-import boto3
-from botocore.exceptions import NoCredentialsError
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
 
 # Initialize PaddleOCR once with updated parameters
 ocr_model = PaddleOCR(use_textline_orientation=True, lang='en')
 
-def upload_image_and_get_url(image_path):
-    """
-    Upload image to AWS S3 and return the public URL.
-    """
-    s3 = boto3.client('s3')
-    bucket_name = os.getenv("AWS_S3_BUCKET_NAME")
-    file_name = os.path.basename(image_path)
-
-    try:
-        s3.upload_file(image_path, bucket_name, file_name, ExtraArgs={'ACL': 'public-read'})
-        image_url = f'https://{bucket_name}.s3.amazonaws.com/{file_name}'
-        return image_url
-    except NoCredentialsError:
-        print("Credentials not available")
-        return None
-
 def analyze_uv_coverage(img, brightness_threshold=150, kernel_size=5, apply_blur=True, adaptive_thresh=False):
     """
     Analyze UV sterilization coverage by thresholding the grayscale image.
+    Optional adaptive thresholding and Gaussian blur for noise reduction.
+    Morphological operations clean the mask for better accuracy.
     """
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
@@ -54,21 +34,24 @@ def analyze_uv_coverage(img, brightness_threshold=150, kernel_size=5, apply_blur
     else:
         _, binary_mask = cv2.threshold(gray, brightness_threshold, 255, cv2.THRESH_BINARY)
 
-    # Morphological operations
+    # Morphological opening (erosion followed by dilation) to remove noise
     kernel = np.ones((kernel_size, kernel_size), np.uint8)
     binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_OPEN, kernel, iterations=1)
+
+    # Morphological closing (dilation followed by erosion) to close small holes inside foreground
     binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel, iterations=1)
 
     total_pixels = binary_mask.size
     sterilized_pixels = cv2.countNonZero(binary_mask)
     coverage_percent = (sterilized_pixels / total_pixels) * 100
 
-    # Create overlay for visualization
+    # Create overlay for visualization: Green = sterilized, Red = unsterilized
     overlay = img.copy()
     overlay[binary_mask == 255] = [0, 255, 0]  # Green
     overlay[binary_mask == 0] = [0, 0, 255]    # Red
 
     annotated_img = cv2.addWeighted(img, 0.6, overlay, 0.4, 0)
+
     return annotated_img, coverage_percent
 
 def create_pdf_report(coverage_percent, extracted_texts, annotated_image_path, output_path):
@@ -99,12 +82,21 @@ def create_pdf_report(coverage_percent, extracted_texts, annotated_image_path, o
 
     pdf.output(output_path)
 
+def upload_image_and_get_url(image_path):
+    """
+    TODO: Implement your image upload to public storage here.
+    For now, returns a placeholder URL.
+    """
+    # Example: upload to AWS S3, Azure Blob Storage, or other service
+    # Return the public URL to the uploaded image
+    return "https://example.com/path/to/your/annotated_image.jpg"
+
 def save_record_to_salesforce(annotated_image_url, coverage_percent, original_image_pil, compliance_threshold=80):
     sf = Salesforce(
-        username=os.getenv('SF_USERNAME'),
-        password=os.getenv('SF_PASSWORD'),
-        security_token=os.getenv('SF_SECURITY_TOKEN'),
-        domain=os.getenv('SF_DOMAIN', 'login')  # 'test' for sandbox
+        username=os.environ['SF_USERNAME'],
+        password=os.environ['SF_PASSWORD'],
+        security_token=os.environ['SF_SECURITY_TOKEN'],
+        domain=os.environ.get('SF_DOMAIN', 'login')  # 'test' for sandbox
     )
     
     # Save original image temporarily, upload it, get URL
@@ -116,7 +108,7 @@ def save_record_to_salesforce(annotated_image_url, coverage_percent, original_im
     os.unlink(temp_orig_img_path)
 
     compliance_status = 'Pass' if coverage_percent >= compliance_threshold else 'Fail'
-    technician_id = os.getenv('SF_TECHNICIAN_ID')  # Salesforce UserId lookup
+    technician_id = os.environ.get('SF_TECHNICIAN_ID')  # Salesforce UserId lookup
     
     record_name = f"UV Verification - {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')}"
 
@@ -124,7 +116,7 @@ def save_record_to_salesforce(annotated_image_url, coverage_percent, original_im
         'Name': record_name,
         'Annotated_Image__c': annotated_image_url,
         'Coverage_Percentage__c': round(coverage_percent, 2),
-        'Original_Image__c': original_image_url,
+        'Original_Image__c': original_image_url,  # Correct field API name here
         'Compliance_Status__c': compliance_status,
         'Technician_ID__c': technician_id,
         'Verified_On__c': datetime.utcnow().isoformat()
@@ -179,11 +171,15 @@ def process_image(input_img, brightness_threshold=150):
 
 iface = gr.Interface(
     fn=process_image,
-    inputs=[gr.Image(type="pil", label="Upload Post-UV Sterilization Image"),
-            gr.Slider(50, 255, value=150, step=1, label="Brightness Threshold")],
-    outputs=[gr.Image(type="numpy", label="Annotated Image"),
-             gr.Textbox(label="UV Sterilization Report", lines=5),
-             gr.File(label="Download PDF Report")],
+    inputs=[
+        gr.Image(type="pil", label="Upload Post-UV Sterilization Image"),
+        gr.Slider(50, 255, value=150, step=1, label="Brightness Threshold")
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),
+        gr.Textbox(label="UV Sterilization Report", lines=5),
+        gr.File(label="Download PDF Report")
+    ],
     title="UV Sterilization Coverage Analyzer",
     description="Upload a post-UV sterilization image to analyze surface coverage and generate a compliance report."
 )