Update app.py

app.py

@@ -1,12 +1,3 @@
-import subprocess
-import sys
-print("Reinstalling mmcv")
-subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "mmcv-full==1.3.17"])
-subprocess.check_call([sys.executable, "-m", "pip", "install", "mmcv-full==1.3.17", "-f", "https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html"])
-print("mmcv install complete") 
-
-## Only works if we reinstall mmcv here.
-
 from gradio.outputs import Label
 from icevision.all import *
 from icevision.models.checkpoint import *
@@ -25,84 +16,24 @@ class_map = checkpoint_and_model["class_map"]
 img_size = checkpoint_and_model["img_size"]
 valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
 
-for root, dirs, files in os.walk(r"sample_images/"):
-    for filename in files:
-        print("Loading sample image:", filename)
-
-
-# Populate examples in Gradio interface
-example_images = [["sample_images/" + file] for file in files]
-# Columns: Input Image | Label | Box | Detection Threshold
-#examples = [
-#    [example_images[0], False, True, 0.5],
-#    [example_images[1], True, True, 0.5],
-#    [example_images[2], False, True, 0.7],
-#    [example_images[3], True, True, 0.7],
-#    [example_images[4], False, True, 0.5],
-#    [example_images[5], False, True, 0.5],
-#    [example_images[6], False, True, 0.6],
-#    [example_images[7], False, True, 0.6],
-#]
-
 examples = [['sample_images/IMG_20191212_151351.jpg'],['sample_images/IMG_20191212_153420.jpg'],['sample_images/IMG_20191212_154100.jpg']]
 
-
-#def show_preds(input_image, display_label, display_bbox, detection_threshold):
 def show_preds(input_image):
-    # if detection_threshold == 0:
-        #detection_threshold = 0.5
     img = PIL.Image.fromarray(input_image, "RGB")
-    
     pred_dict  = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.5,
                                            display_label=False, display_bbox=True, return_img=True, 
                                            font_size=16, label_color="#FF59D6")
                                            
-    #pred_dict = model_type.end2end_detect(
-    #    img,
-    #    valid_tfms,
-    #    model,
-    #    class_map=class_map,
-    #    detection_threshold=detection_threshold,
-    #    display_label=display_label,
-    #    display_bbox=display_bbox,
-    #    return_img=True,
-    #    font_size=16,
-    #    label_color="#FF59D6",
-    #)
-    
     return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
 
 
-# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
-display_chkbox_label = gr.inputs.Checkbox(label="Label", default=False)
-display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
-detection_threshold_slider = gr.inputs.Slider(
-    minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold"
-)
-outputs = [
-    gr.outputs.Image(type="pil", label="RetinaNet Inference"),
-    gr.outputs.Textbox(type="number", label="Microalgae Count"),
-]
-
-article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
-
-# Option 1: Get an image from local drive
 gr_interface = gr.Interface(
     fn=show_preds,
-    inputs=[
-        "image"#,
-        #display_chkbox_label,
-        #display_chkbox_box,
-        #detection_threshold_slider,
-    ],
-    outputs=outputs,
+    inputs=["image"],
+    outputs=[gr.outputs.Image(type="pil", label="RetinaNet Inference"), gr.outputs.Textbox(type="number", label="Microalgae Count")],
     title="Microalgae Detector with RetinaNet",
     description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.",
-    article=article,
+    article="<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>",
     examples=examples,
 )
-# #  Option 2: Grab an image from a webcam
-# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
-# #  Option 3: Continuous image stream from the webcam
-# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
 gr_interface.launch()