Update app.py

app.py

@@ -12,15 +12,19 @@ import base64
 import io
 import logging
 
+# Set up logging to debug file writing issues
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Initialize PaddleOCR once with updated parameters
 ocr_model = PaddleOCR(use_textline_orientation=True, lang='en')
 
 def analyze_uv_coverage(img, brightness_threshold=150, kernel_size=5, apply_blur=True, adaptive_thresh=False):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
     if apply_blur:
         gray = cv2.GaussianBlur(gray, (5, 5), 0)
+
     if adaptive_thresh:
         binary_mask = cv2.adaptiveThreshold(
             gray, 255,
@@ -29,40 +33,56 @@ def analyze_uv_coverage(img, brightness_threshold=150, kernel_size=5, apply_blur
             11, 2)
     else:
         _, binary_mask = cv2.threshold(gray, brightness_threshold, 255, cv2.THRESH_BINARY)
+
+    # 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: Green = sterilized, Red = unsterilized
     overlay = img.copy()
-    overlay[binary_mask == 255] = [0, 255, 0]
-    overlay[binary_mask == 0] = [0, 0, 255]
+    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):
     pdf = FPDF()
     pdf.add_page()
+
     pdf.set_font("Arial", 'B', 16)
     pdf.cell(200, 10, txt="UV Sterilization Report", ln=True, align='C')
     pdf.ln(10)
+
     pdf.set_font("Arial", size=12)
     pdf.cell(0, 10, f"Sterilization Coverage: {coverage_percent:.2f}%", ln=True)
     pdf.ln(5)
+
     pdf.cell(0, 10, "Extracted Text from Image (OCR):", ln=True)
     pdf.set_font("Arial", size=10)
     if extracted_texts:
         for text in extracted_texts:
+            # Filter out very short or empty OCR texts to improve clarity
             if len(text.strip()) > 1:
                 pdf.multi_cell(0, 8, f"- {text}")
     else:
         pdf.cell(0, 8, "No text detected.", ln=True)
+
     pdf.ln(10)
     pdf.cell(0, 10, "Annotated Image:", ln=True)
     pdf.image(annotated_image_path, x=10, y=pdf.get_y(), w=pdf.w - 20)
+
     pdf.output(output_path)
 
+# New function to upload image to Salesforce and get URL (adapted from reference code)
 def upload_image_to_salesforce(image_path, image_name, record_id=None):
     try:
         sf = Salesforce(
@@ -71,19 +91,31 @@ def upload_image_to_salesforce(image_path, image_name, record_id=None):
             security_token=os.environ['SF_SECURITY_TOKEN'],
             domain=os.environ.get('SF_DOMAIN', 'login')
         )
+        logger.debug(f"Uploading image {image_name} for record ID: {record_id}")
+
+        # Read the image file and encode it as base64
         with open(image_path, "rb") as f:
             image_data = f.read()
+
         encoded_image_data = base64.b64encode(image_data).decode('utf-8')
+
+        # Create a ContentVersion in Salesforce
         content_version_data = {
             "Title": image_name,
             "PathOnClient": image_name,
             "VersionData": encoded_image_data,
         }
+        
         if record_id:
             content_version_data["FirstPublishLocationId"] = record_id
+
         content_version = sf.ContentVersion.create(content_version_data)
         content_version_id = content_version["id"]
+        logger.info(f"Image uploaded to Salesforce with ContentVersion ID: {content_version_id}")
+
+        # Generate the public URL for the image
         image_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{content_version_id}"
+        logger.debug(f"Generated image URL: {image_url}")
         return image_url
     except Exception as e:
         logger.error(f"Error uploading image to Salesforce: {str(e)}", exc_info=True)
@@ -92,7 +124,9 @@ def upload_image_to_salesforce(image_path, image_name, record_id=None):
 def upload_image_and_get_url(image_path):
     from datetime import datetime
     import uuid
+
     unique_filename = f"{uuid.uuid4().hex}_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.jpg"
+    
     try:
         image_url = upload_image_to_salesforce(image_path, unique_filename)
         return image_url
@@ -105,16 +139,22 @@ def save_record_to_salesforce(annotated_image_url, coverage_percent, original_im
         username=os.environ['SF_USERNAME'],
         password=os.environ['SF_PASSWORD'],
         security_token=os.environ['SF_SECURITY_TOKEN'],
-        domain=os.environ.get('SF_DOMAIN', 'login')
+        domain=os.environ.get('SF_DOMAIN', 'login')  # 'test' for sandbox
     )
+    
+    # Save original image temporarily, upload it, get URL
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_orig_img_file:
         original_image_pil.save(temp_orig_img_file.name, format="JPEG")
         temp_orig_img_path = temp_orig_img_file.name
-        original_image_url = upload_image_and_get_url(temp_orig_img_path)
+    
+    original_image_url = upload_image_and_get_url(temp_orig_img_path)
     os.unlink(temp_orig_img_path)
+
     compliance_status = 'Pass' if coverage_percent >= compliance_threshold else 'Fail'
-    technician_id = os.environ.get('SF_TECHNICIAN_ID')
+    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,
@@ -125,17 +165,20 @@ def save_record_to_salesforce(annotated_image_url, coverage_percent, original_im
         'Verified_On__c': datetime.utcnow().isoformat()
     })
 
-def process_image(input_img, brightness_threshold=150):
-    img = np.array(input_img)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+def process_image(input_img, brightness_threshold=150, slider_state=gr.State()):
+    img = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)
+
+    # Resize large images for faster processing, preserving aspect ratio
     max_dim = 640
     h, w = img.shape[:2]
     if max(h, w) > max_dim:
         scale = max_dim / max(h, w)
         img = cv2.resize(img, (int(w * scale), int(h * scale)))
+
     start_time = time.time()
-    ocr_result = ocr_model.predict(img)
+    ocr_result = ocr_model.ocr(img)
     ocr_time = time.time() - start_time
+
     extracted_texts = []
     for line in ocr_result:
         if line:
@@ -143,44 +186,48 @@ def process_image(input_img, brightness_threshold=150):
                 text = word_info[1][0].strip()
                 if len(text) > 1:
                     extracted_texts.append(text)
+
     annotated_img, coverage_percent = analyze_uv_coverage(img, brightness_threshold)
+
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img_file:
         cv2.imwrite(temp_img_file.name, annotated_img)
         annotated_img_path = temp_img_file.name
+
     temp_pdf_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     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}%"
-    os.unlink(annotated_img_path)
-    return annotated_img_rgb, report_text, temp_pdf_file.name
 
-with gr.Blocks() as iface:
-    gr.Markdown("""# UV Sterilization Coverage Analyzer
-Upload a post-UV sterilization image to analyze surface coverage and generate a compliance report.""")
+    annotated_img_rgb = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
 
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Post-UV Sterilization Image")
-        image_output = gr.Image(type="numpy", label="Annotated Image")
+    report_text = f"UV Sterilization Coverage: {coverage_percent:.2f}%"
 
-    slider = gr.Slider(50, 255, value=150, step=1, label="Brightness Threshold")
-    report_output = gr.Textbox(label="UV Sterilization Report", lines=5)
-    pdf_output = gr.File(label="Download PDF Report")
+    # Clean up temp image file after PDF generation
+    os.unlink(annotated_img_path)
 
-    with gr.Row():
-        clear_btn = gr.Button("Clear")
-        submit_btn = gr.Button("Submit", variant="primary")
+    # Store the current slider value for future image uploads
+    slider_state = brightness_threshold
 
-    def reset_slider(img):
-        return 150
+    return annotated_img_rgb, report_text, temp_pdf_file.name, slider_state
 
-    image_input.change(fn=reset_slider, inputs=image_input, outputs=slider)
-    submit_btn.click(fn=process_image, inputs=[image_input, slider], outputs=[image_output, report_output, pdf_output])
-    clear_btn.click(lambda: [None, None, 150, "", None], outputs=[image_input, image_output, slider, report_output, pdf_output])
+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", interactive=True)],
+    outputs=[gr.Image(type="numpy", label="Annotated Image"),
+             gr.Textbox(label="UV Sterilization Report", lines=5),
+             gr.File(label="Download PDF Report"),
+             gr.State()],
+    title="UV Sterilization Coverage Analyzer",
+    description="Upload a post-UV sterilization image to analyze surface coverage and generate a compliance report."
+)
 
-iface.queue()
+iface.queue()  # Enable request queuing to improve UX on heavy processing
 
 if __name__ == "__main__":
     iface.launch()