diff --git a/trt.py b/trt.py
new file mode 100644
index 0000000..9bc51f4
--- /dev/null
+++ b/trt.py
@@ -0,0 +1,125 @@
+#!/usr/bin/python3
+"""
+Expose Jetson's TRT models via a rest API.
+Based on Dr Robin Cole's Tensorflow-lite-rest-server:
+https://github.com/robmarkcole/tensorflow-lite-rest-server
+"""
+import io
+import sys
+
+import numpy as np
+import cv2
+import jetson.inference
+import jetson.utils
+from fastapi import FastAPI, File, HTTPException, UploadFile
+
+from datetime import datetime
+
+
+# from helpers import classify_image, read_labels, set_input_tensor
+
+app = FastAPI()
+
+# Settings
+MIN_CONFIDENCE = 0.3  # The absolute lowest confidence for a detection.
+# URL
+FACE_DETECTION_URL = "/v1/vision/face"
+OBJ_DETECTION_URL = "/v1/vision/detection"
+
+
+odNet = jetson.inference.detectNet("ssd-mobilenet-v2")   #, sys.argv, opt.threshold)
+fdNet = jetson.inference.detectNet("facenet")
+
+
+
+@app.get("/")
+async def info():
+    return """tflite-server docs at ip:port/docs"""
+
+
+@app.post(FACE_DETECTION_URL)
+async def predict_face(image: UploadFile = File(...)):
+    try:
+        contents = await image.read()
+        # image_bytes = Image.open(io.BytesIO(contents))
+
+
+        i = np.frombuffer(contents, dtype=np.uint8)
+        im = cv2.imdecode(i, cv2.IMREAD_UNCHANGED)
+        # cv2.imwrite(datetime.now().strftime('%m%d_%H%M%S%f')+'.jpg',im)
+
+        tsr_imga = cv2.cvtColor(im, cv2.COLOR_BGR2RGBA)
+        cudaImage = jetson.utils.cudaFromNumpy (tsr_imga)
+
+        detections = fdNet.Detect(cudaImage, im.shape[0], im.shape[1]) 
+        # jetson.utils.saveImage(datetime.now().strftime('%m%d_%H%M%S%f')+'.jpg', cudaImage)
+        
+        
+        data = {"success": False}
+
+
+        if detections:
+            
+            preds = []
+            for detection in detections:
+                preds.append(
+                    {
+                        "confidence": float(detection.Confidence),
+                        "label": fdNet.GetClassDesc(detection.ClassID), 
+                        "y_min": int(detection.Top),    
+                        "x_min": int(detection.Left),   
+                        "y_max": int(detection.Bottom), 
+                        "x_max": int(detection.Right), 
+                    }
+                )
+            data["predictions"] = preds
+            data["success"] = True
+
+        return data
+    except:
+        e = sys.exc_info()[1]
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post(OBJ_DETECTION_URL)
+async def predict_object(image: UploadFile = File(...)):
+    try:
+        contents = await image.read()
+
+        i = np.frombuffer(contents, dtype=np.uint8)
+        im = cv2.imdecode(i, cv2.IMREAD_UNCHANGED)
+        # cv2.imwrite(datetime.now().strftime('%m%d_%H%M%S%f')+'.jpg',im)
+
+
+        tsr_imga = cv2.cvtColor(im, cv2.COLOR_BGR2RGBA)
+        cudaImage = jetson.utils.cudaFromNumpy (tsr_imga)
+
+        detections = odNet.Detect(cudaImage, im.shape[0], im.shape[1]) 
+        # jetson.utils.saveImage(datetime.now().strftime('%m%d_%H%M%S%f')+'.jpg', cudaImage)
+    
+
+        data = {"success": False}
+        if detections:
+            
+            preds = []
+            for detection in detections:
+                preds.append(
+                    {
+                        "confidence": float(detection.Confidence),
+                        "label": odNet.GetClassDesc(detection.ClassID), 
+                        "y_min": int(detection.Top),    
+                        "x_min": int(detection.Left),   
+                        "y_max": int(detection.Bottom), 
+                        "x_max": int(detection.Right),  
+                    }
+                )
+            data["predictions"] = preds
+            data["success"] = True
+
+        return data
+
+    except:
+        e = sys.exc_info()[1]
+        raise HTTPException(status_code=500, detail=str(e))
+
+