add the Base64-encoded images to the final JSON response

app.py

@@ -79,28 +79,41 @@ def run_leukocoria_prediction(iris_crop):
 # --- 3. FastAPI Application ---
 app = FastAPI()
 
+# In app.py - an updated full_detection_pipeline function
+
 @app.post("/detect/")
 async def full_detection_pipeline(image: UploadFile = File(...)):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
         contents = await image.read()
         tmp.write(contents)
         temp_image_path = tmp.name
-    
+
     try:
+        raw_image = cv2.imread(temp_image_path)
+        if raw_image is None:
+            return JSONResponse(status_code=400, content={"error": "Could not read uploaded image."})
+
         if not detect_faces_roboflow(temp_image_path):
             return JSONResponse(status_code=400, content={"error": "No face detected."})
 
-        raw_image = cv2.imread(temp_image_path)
         eye_crops = detect_eyes_roboflow(temp_image_path, raw_image)
-
         if len(eye_crops) != 2:
-            return JSONResponse(status_code=400, content={"error": "Exactly two eyes not detected."})
-        
+            return JSONResponse(status_code=200, content={"warnings": ["Exactly two eyes not detected."]})
+
         eye_crops.sort(key=lambda c: cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY))[0])
 
+        # Prepare to store all our results
         flags = {}
+        eye_images_b64 = {}
+
         for i, eye_crop in enumerate(eye_crops):
             side = "left" if i == 0 else "right"
+
+            # --- NEW: Encode the cropped eye image to Base64 ---
+            is_success, buffer = cv2.imencode(".jpg", eye_crop)
+            if is_success:
+                eye_images_b64[side] = "data:image/jpeg;base64," + base64.b64encode(buffer).decode("utf-8")
+
             pred = get_largest_iris_prediction(eye_crop)
             if pred:
                 x1, y1 = int(pred['x'] - pred['width'] / 2), int(pred['y'] - pred['height'] / 2)
@@ -110,8 +123,13 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
                 flags[side] = has_leuko
             else:
                 flags[side] = None
-        
-        return JSONResponse(content={"leukocoria": flags, "warnings": []})
+
+        # --- NEW: Include the images in the final response ---
+        return JSONResponse(content={
+            "leukocoria": flags,
+            "warnings": [],
+            "two_eyes": eye_images_b64 # Add the eye images here
+        })
 
     finally:
         os.remove(temp_image_path)