Update api.py

api.py

@@ -83,6 +83,9 @@ from typing import Optional
 from PIL import Image
 import logging
 import io
+import cv2
+import numpy as np
+import base64
 
 logging.basicConfig(
     level=logging.INFO,
@@ -106,6 +109,13 @@ logger.info("TextFilterService initialized.")
 image_classifier = ImageClassifier()
 logger.info("ImageClassifier initialized.")
 
+def blur_image(pil_image: Image.Image) -> Image.Image:
+    """Convert PIL image to OpenCV, apply Gaussian blur, and return as PIL."""
+    cv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+    blurred_cv = cv2.GaussianBlur(cv_image, (25, 25), 0)
+    blurred_pil = Image.fromarray(cv2.cvtColor(blurred_cv, cv2.COLOR_BGR2RGB))
+    return blurred_pil
+
 @app.route("/filtercomment", methods=["POST"])
 def filter_comment():
     text = request.form.get("text")
@@ -119,16 +129,52 @@ def filter_comment():
     if image_file:
         try:
             logger.info("Processing uploaded image file: %s", image_file.filename)
-            image_bytes = image_file.read()
+            image_bytes = await image_file.read()
             image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+            
             result = image_classifier.classify(image)
+            
             if result.get("text"):
                 result['toxic_result'] = text_filter_service.process_text(result.get("text"))
+
             logger.info("Image classification result: %s", result)
-            return jsonify({"image_classification": result})
+
+            # Check if content is toxic or unsafe
+            is_toxic = result.get("toxic") is True or not result.get("toxic_result", {}).get("safe", True)
+            
+            if is_toxic:
+                logger.info("Toxic content detected. Blurring image.")
+                blurred_image = blur_image(image)
+
+                # Compose annotation message
+                if not result.get("text"):
+                    message = "Toxic content is present\nImage is blurred"
+                else:
+                    toxic_result = result.get("toxic_result", {})
+                    exclude_keys = {"safe", "identity_hate_custom", "not_identity_hate_custom"}
+                    filtered = {k: v for k, v in toxic_result.items() if k not in exclude_keys}
+                    if filtered:
+                        max_label = max(filtered, key=filtered.get)
+                        message = f"The image contains {max_label} ({filtered[max_label]:.2f}) content"
+                    else:
+                        message = "Toxic content is present\nImage is blurred"
+
+                # Encode blurred image to base64
+                buffer = io.BytesIO()
+                blurred_image.save(buffer, format="JPEG")
+                encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+                result["blurred_image_base64"] = encoded_image
+                result["blurred"] = True
+                result["alert_message"] = message
+            else:
+                result["blurred"] = False
+
+            return JSONResponse(content={"image_classification": result})
+
         except Exception as e:
             logger.error("Image file processing failed: %s", str(e))
-            return jsonify({"error": f"Image file processing failed: {str(e)}"}), 400
+            return JSONResponse(status_code=400, content={"error": f"Image file processing failed: {str(e)}"})
 
     # Case 2: Extract text from image URL
     if image_url: