Create app.py

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 = "Detect defects in bird nest jar"
+models_ids = ['linhcuem/defects_nest_jar_yolov5']
+
+current_model_id = models_ids[-1]
+model = yolov5.load(current_model_id)
+
+examples = [['test_images/16823291638707408-a2A2448-23gmBAS_40174045.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823292102253310-a2A2448-23gmBAS_40174046.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823291808953550-a2A2448-23gmBAS_40174048.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823291801532480-a2A2448-23gmBAS_40174048.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5']]
+
+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="Do anh Dat",
+    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 Fale,
+).launch(enable_queue=True)