Create app.py

app.py

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+try:
+    import detectron2
+except:
+    import os
+
+    os.system("pip install git+https://github.com/facebookresearch/detectron2.git")
+
+import cv2
+
+from matplotlib.pyplot import axis
+import gradio as gr
+import requests
+import numpy as np
+from torch import nn
+import requests
+
+import torch
+
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog
+
+
+models = [
+    {
+        "name": "Version 1 (2-class)",
+        "model_path": "https://huggingface.co/dbmdz/detectron2-model/resolve/main/model_final.pth",
+        "classes": ["footer"],
+        "cfg": None,
+        "metadata": None,
+    },
+]
+
+model_name_to_id = {model["name"]: id_ for id_, model in enumerate(models)}
+
+for model in models:
+    model["cfg"] = get_cfg()
+    model["cfg"].merge_from_file("./configs/detectron2/faster_rcnn_R_50_FPN_3x.yaml")
+    model["cfg"].MODEL.ROI_HEADS.NUM_CLASSES = len(model["classes"])
+    model["cfg"].MODEL.WEIGHTS = model["model_path"]
+
+    model["metadata"] = MetadataCatalog.get(model["name"])
+    model["metadata"].thing_classes = model["classes"]
+
+    if not torch.cuda.is_available():
+        model["cfg"].MODEL.DEVICE = "cpu"
+
+
+def inference(image_url, image, min_score, model_name):
+    if image_url:
+        r = requests.get(image_url)
+        if r:
+            im = np.frombuffer(r.content, dtype="uint8")
+            im = cv2.imdecode(im, cv2.IMREAD_COLOR)
+    else:
+        # Model expect BGR!
+        im = image[:, :, ::-1]
+
+    model_id = model_name_to_id[model_name]
+
+    models[model_id]["cfg"].MODEL.ROI_HEADS.SCORE_THRESH_TEST = min_score
+    predictor = DefaultPredictor(models[model_id]["cfg"])
+
+    outputs = predictor(im)
+
+    v = Visualizer(im, models[model_id]["metadata"], scale=1.2)
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+
+    return out.get_image()
+
+
+title = "# DBMDZ Detectron2 Model Demo"
+description = """
+This demo introduces an interactive playground for our trained Detectron2 model.
+"""
+footer = "Made in Egypt with ❤️."
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Tab("From URL"):
+        url_input = gr.Textbox(
+            label="Image URL",
+            placeholder="https://api.digitale-sammlungen.de/iiif/image/v2/bsb10483966_00008/full/500,/0/default.jpg",
+        )
+
+    with gr.Tab("From Image"):
+        image_input = gr.Image(type="numpy", label="Input Image")
+
+    min_score = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Minimum score")
+
+    model_name = gr.Radio(
+        choices=[model["name"] for model in models],
+        value=models[0]["name"],
+        label="Select Detectron2 model",
+    )
+
+    output_image = gr.Image(type="pil", label="Output")
+
+    inference_button = gr.Button("Submit")
+
+    inference_button.click(
+        fn=inference,
+        inputs=[url_input, image_input, min_score, model_name],
+        outputs=output_image,
+    )
+
+    gr.Markdown(footer)
+
+demo.launch()