Create app.py

app.py

@@ -0,0 +1,41 @@
+from gradio.outputs import Label
+from icevision.all import *
+from icevision.models.checkpoint import *
+import PIL
+import gradio as gr
+import os
+
+# Load model
+checkpoint_path = "model_checkpoint.pth"
+checkpoint_and_model = model_from_checkpoint(checkpoint_path)
+model = checkpoint_and_model["model"]
+model_type = checkpoint_and_model["model_type"]
+class_map = checkpoint_and_model["class_map"]
+
+# Transforms
+img_size = checkpoint_and_model["img_size"]
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
+
+examples = [['1.jpg'],['2.jpg'],['3.jpg']]
+
+def show_preds(input_image):
+    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")
+                                           
+    return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
+
+
+gr_interface = gr.Interface(
+    fn=show_preds,
+    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="<p style='text-align: center'><a href='https://dicksonneoh.com/portfolio/how_to_deploy_od_models_on_android_with_flutter/' target='_blank'>Blog post</a></p>",
+    examples=examples,
+    theme="dark-grass",
+    enable_queue=True
+)
+gr_interface.launch()