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README.md

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 ---
-title: Clash Of Clans Object Detection
-emoji: 👁
-colorFrom: purple
+title: Clash of Clans Object Detection
+emoji: 🎮
+colorFrom: red
 colorTo: gray
 sdk: gradio
 sdk_version: 3.15.0

app.py

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+
+import json
+import gradio as gr
+import yolov5
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+app_title = "Clash of Clans Object Detection"
+models_ids = ['keremberke/yolov5n-clash-of-clans', 'keremberke/yolov5s-clash-of-clans', 'keremberke/yolov5m-clash-of-clans']
+article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>huggingface.co/{models_ids[-1]}</a> | <a href='https://huggingface.co/keremberke/clash-of-clans-object-detection'>huggingface.co/keremberke/clash-of-clans-object-detection</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>"
+
+current_model_id = models_ids[-1]
+model = yolov5.load(current_model_id)
+
+examples = [['test_images/IMG_1879_jpg.rf.c0e9cd93962f7cf2df6fbeef63ab6ed9.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans'], ['test_images/IMG_1906_jpg.rf.e3d1ef9a4c55d6576e95ba057984d204.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans'], ['test_images/IMG_1931_jpg.rf.16f42f6d309c3de5661625af454aeb0c.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans'], ['test_images/IMG_2016_jpg.rf.6013a2119e90b56bb2a07d83954dc637.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans'], ['test_images/IMG_2023_jpg.rf.01d180bc2ed3d5bab7b0026cd8e4c09a.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans'], ['test_images/IMG_2034_jpg.rf.40ab3489487d018d0112f49622f0f9a0.jpg', 0.25, 'keremberke/yolov5m-clash-of-clans']]
+
+
+def predict(image, threshold=0.25, model_id=None):
+    # update model if required
+    global current_model_id
+    global model
+    if model_id != current_model_id:
+        model = yolov5.load(model_id)
+        current_model_id = model_id
+
+    # get model input size
+    config_path = hf_hub_download(repo_id=model_id, filename="config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    input_size = config["input_size"]
+
+    # perform inference
+    model.conf = threshold
+    results = model(image, size=input_size)
+    numpy_image = results.render()[0]
+    output_image = Image.fromarray(numpy_image)
+    return output_image
+
+
+gr.Interface(
+    title=app_title,
+    description="Created by 'keremberke'",
+    article=article,
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(maximum=1, step=0.01, value=0.25),
+        gr.Dropdown(models_ids, value=models_ids[-1]),
+    ],
+    outputs=gr.Image(type="pil"),
+    examples=examples,
+    cache_examples=True if examples else False,
+).launch(enable_queue=True)

requirements.txt

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+
+yolov5==7.0.5
+gradio==3.15.0
+torch
+huggingface-hub