diff --git a/README.md b/README.md
old mode 100644
new mode 100755
index b91c095..caceb53
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@ $ python3 coral-app.py --models-directory models
 ```
 Then use curl to query:
 ```
-curl -X POST -F image=@images/test-image3.jpg 'http://localhost:5000/v1/vision/detection'
+curl -X POST -F image=@images/test-image3.jpg -F min_confidence=.5 'http://localhost:5000/v1/vision/detection'
 
 {'predictions': [{'confidence': 0.953125,
    'label': 'person',
diff --git a/coral-app.py b/coral-app.py
index 213b9cd..7202282 100644
--- a/coral-app.py
+++ b/coral-app.py
@@ -7,6 +7,7 @@
 import io
 import os
 import logging
+import time
 
 import flask
 from PIL import Image
@@ -39,39 +40,42 @@ def predict():
     data = {"success": False}
 
     if flask.request.method == "POST":
+        if flask.request.form.get('min_confidence'):
+            threshold=float(flask.request.form['min_confidence'])
+        else:
+            threshold=float(0.4)
         if flask.request.files.get("image"):
             image_file = flask.request.files["image"]
             image_bytes = image_file.read()
             image = Image.open(io.BytesIO(image_bytes))
-
-            size = common.input_size(interpreter)
-            image = image.convert("RGB").resize(size, Image.ANTIALIAS)
-
-            # Run an inference
-            common.set_input(interpreter, image)
-            interpreter.invoke()
             _, scale = common.set_resized_input(
                 interpreter, image.size, lambda size: image.resize(size, Image.ANTIALIAS))
-
-            threshold=0.4
+            
+            # Run an inference
+            #start = time.perf_counter()
+            interpreter.invoke()
+            #inference_time = time.perf_counter() - start
             objs = detect.get_objects(interpreter, threshold, scale)
-
+            #logging.debug('%.2f ms' % (inference_time * 1000))
             if objs:
                 data["success"] = True
                 preds = []
 
                 for obj in objs:
-                    preds.append(
-                        {
-                            "confidence": float(obj.score),
-                            "label": labels[obj.id],
-                            "y_min": int(obj.bbox[1]),
-                            "x_min": int(obj.bbox[0]),
-                            "y_max": int(obj.bbox[3]),
-                            "x_max": int(obj.bbox[2]),
-                        }
-                    )
+                    if float(obj.score) >= float(threshold):
+                        preds.append(
+                            {
+                                "confidence": float(obj.score),
+                                "label": labels[obj.id],
+                                "y_min": int(obj.bbox.ymin),
+                                "x_min": int(obj.bbox.xmin),
+                                "y_max": int(obj.bbox.ymax),
+                                "x_max": int(obj.bbox.xmax),
+                            }
+                        )
                 data["predictions"] = preds
+            else:
+                logging.debug('No objects detected')
 
     # return the data dictionary as a JSON response
     return flask.jsonify(data)
@@ -107,6 +111,6 @@ def predict():
     global interpreter
     interpreter = edgetpu.make_interpreter(model_file)
     interpreter.allocate_tensors()
-    print("\n Initialised interpreter with model : {}".format(model_file))
+    logging.debug("\n Initialised interpreter with model : {}".format(model_file))
 
-    app.run(host="0.0.0.0", debug=True, port=args.port)
\ No newline at end of file
+    app.run(host="0.0.0.0", debug=False, port=args.port)
\ No newline at end of file