Update app.py

app.py

@@ -6,6 +6,10 @@ import tempfile
 import os
 from paddleocr import PaddleOCR
 import time
+from simple_salesforce import Salesforce
+from datetime import datetime
+import base64
+import io
 
 # Initialize PaddleOCR once with updated parameters
 ocr_model = PaddleOCR(use_textline_orientation=True, lang='en')
@@ -78,6 +82,43 @@ 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.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
+    )
+    
+    # Encode original image to base64 string for storage
+    buffered = io.BytesIO()
+    original_image_pil.save(buffered, format="JPEG")
+    original_img_b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+    compliance_status = 'Pass' if coverage_percent >= compliance_threshold else 'Fail'
+    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')}"
+
+    sf.UV_Verification__c.create({
+        'Name': record_name,
+        'Annotated_Image__c': annotated_image_url,
+        'Coverage_Percentage__c': round(coverage_percent, 2),
+        'Original_Image__c': original_img_b64,
+        'Compliance_Status__c': compliance_status,
+        'Technician_ID__c': technician_id,
+        'Verified_On__c': datetime.utcnow().isoformat()
+    })
+
 def process_image(input_img, brightness_threshold=150):
     img = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)
 
@@ -96,7 +137,6 @@ def process_image(input_img, brightness_threshold=150):
     for line in ocr_result:
         if line:
             for word_info in line:
-                # Filter short strings and whitespace only
                 text = word_info[1][0].strip()
                 if len(text) > 1:
                     extracted_texts.append(text)
@@ -111,11 +151,15 @@ def process_image(input_img, brightness_threshold=150):
     temp_pdf_file.close()
     create_pdf_report(coverage_percent, extracted_texts, annotated_img_path, temp_pdf_file.name)
 
+    # Upload annotated image and get URL
+    annotated_image_url = upload_image_and_get_url(annotated_img_path)
+
+    # Save record in Salesforce
+    save_record_to_salesforce(annotated_image_url, coverage_percent, input_img)
+
     annotated_img_rgb = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
 
     report_text = f"UV Sterilization Coverage: {coverage_percent:.2f}%"
-    # Optionally include OCR time for debugging:
-    # report_text += f"\nOCR Processing Time: {ocr_time:.2f} seconds"
 
     # Clean up temp image file after PDF generation
     os.unlink(annotated_img_path)