diff --git a/ai_ta_backend/modal/pest_detection.py b/ai_ta_backend/modal/pest_detection.py
new file mode 100644
index 00000000..9d7a1ad0
--- /dev/null
+++ b/ai_ta_backend/modal/pest_detection.py
@@ -0,0 +1,154 @@
+"""
+Run with: $ modal serve ai_ta_backend/modal/pest_detection.py
+Deploy with with: $ modal deploy ai_ta_backend/modal/pest_detection.py
+
+Just send a post request here: https://kastanday--v2-pest-detection-yolo-model-predict.modal.run/
+with body: 
+{
+    "image_urls": [
+        "https://www.arborday.org/trees/health/pests/images/figure-whiteflies-1.jpg",
+        "https://www.arborday.org/trees/health/pests/images/figure-japanese-beetle-3.jpg"
+    ]
+}
+
+Inspired by https://modal.com/docs/examples/webcam#prediction-function
+"""
+import inspect
+import json
+import os
+import traceback
+import uuid
+from tempfile import NamedTemporaryFile
+from typing import List
+
+import modal
+from fastapi import Request
+from modal import Secret, Stub, build, enter, web_endpoint
+
+# Simpler image, but slower cold starts: modal.Image.from_registry('ultralytics/ultralytics:latest-cpu')
+image = (
+    modal.Image.debian_slim(python_version="3.10").apt_install("libgl1-mesa-glx", "libglib2.0-0")
+    # .run_commands(["apt-get install -y libgl1-mesa-glx libglib2.0-0 wget"])
+    .pip_install(
+        "opencv-python",
+        "torch==2.2.0",
+        "ultralytics==8.1.0",
+        "torchvision==0.17.0",
+        "boto3==1.28.79",
+        "fastapi==0.109.2",
+        "pillow",
+    ))
+stub = Stub("v2_pest_detection_yolo", image=image)
+
+# Imports needed inside the image
+with image.imports():
+  import inspect
+  import os
+  import traceback
+  import uuid
+  from tempfile import NamedTemporaryFile
+  from typing import List
+
+  import boto3
+  import requests
+  from PIL import Image
+  from ultralytics import YOLO
+
+
+@stub.cls(cpu=1, image=image, secrets=[Secret.from_name("uiuc-chat-aws")])
+class Model:
+  """
+  1. Build (bake things into the image for faster subsequent startups)
+  2. Enter (run once per container start)
+  3. Web Endpoint (serve a web endpoint)
+  """
+
+  @build()
+  def download_model(self):
+    """Model weights are downloaded once at image build time using the @build hook and saved into the image. 'Baking' models into the modal.Image at build time provided the fastest cold start. """
+    model_url = "https://assets.kastan.ai/pest_detection_model_weights.pt"
+    response = requests.get(model_url)
+
+    model_path = "/cache/pest_detection_model_weights.pt"
+    os.makedirs("/cache/", exist_ok=True)
+    with open(model_path, 'wb') as f:
+      f.write(response.content)
+
+  @enter()
+  def run_this_on_container_startup(self):
+    """Runs once per container start. Like __init__ but for the container."""
+    self.model = YOLO('/cache/pest_detection_model_weights.pt')
+    self.s3_client = boto3.client(
+        's3',
+        aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
+        aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY'),
+    )
+
+  @web_endpoint(method="POST")
+  async def predict(self, request: Request):
+    """
+    This used to use the method decorator
+    Run the pest detection plugin on an image.
+    """
+    print("Inside predict() endpoint")
+
+    print("Request: ", request)
+    input = await request.json()
+    print("Request.json(): ", input)
+    image_urls = input.get('image_urls', [])
+    print(f"Image URLS (no parsing): {image_urls}")
+
+    # image_urls = json.loads(image_urls)
+    # print(f"json parsed Image URLS: {image_urls}")
+
+    try:
+      # Run the plugin
+      annotated_images = self._detect_pests(image_urls)
+      print(f"annotated_images found: {len(annotated_images)}")
+      results = []
+      # Generate a unique ID for the request
+      unique_id = uuid.uuid4()
+      # self.posthog.capture('distinct_id_of_the_user',
+      #                      event='run_pest_detection_invoked',
+      #                      properties={
+      #                          'image_urls': image_urls,
+      #                          'unique_id': unique_id,
+      #                      })
+      for index, image in enumerate(annotated_images):
+        # Infer the file extension from the image URL or set a default
+        file_extension = '.png'
+        image_format = file_extension[1:].upper()
+
+        with NamedTemporaryFile(mode='wb', suffix=file_extension) as tmpfile:
+          # Save the image with the specified format
+          image.save(tmpfile, format=image_format)
+          tmpfile.flush()  # Ensure all data is written to the file
+          tmpfile.seek(0)  # Move the file pointer to the start of the file
+          # Include UUID and index in the s3_path
+          s3_path = f'pest_detection/annotated-{unique_id}-{index}{file_extension}'
+          # Upload the file to S3
+          with open(tmpfile.name, 'rb') as file_data:
+            self.s3_client.upload_fileobj(Fileobj=file_data, Bucket=os.getenv('S3_BUCKET_NAME'), Key=s3_path)
+          results.append(s3_path)
+      return results
+    except Exception as e:
+      err = f"❌❌ Error in (pest_detection): `{inspect.currentframe().f_code.co_name}`: {e}\nTraceback:\n{traceback.format_exc()}"  # type: ignore
+      print(err)
+      # sentry_sdk.capture_exception(e)
+      return err
+
+  def _detect_pests(self, image_paths: List[str]) -> List[Image.Image]:
+    """ Run pest detection on the given images. """
+    # Run inference
+    results = self.model(image_paths)  # results object with inference results
+
+    annotated_images = []
+
+    # Extract annotated images from the results object
+    # Flatten the results object to get the annotated images for each input image
+    for result_set in results:
+      for r in result_set:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image (annotated with bounding boxes and class labels)
+        annotated_images.append(im)
+    return annotated_images