Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+import yolov9
+
+class Inference_Nascent_Spawning_Deriving_From_YOLOv9:
+    def __init__(self):
+        self.model = None
+        self.model_path = None
+        self.image_size = None 
+        self.conf_threshold = None 
+        self.iou_threshold = None 
+        
+    # Object behavior / Method -> 1
+    def download_models(self, model_id):
+        hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
+        return f"./{model_id}"
+    
+    # Object behavior / Method -> 2
+    def load_model(self, model_id):
+        self.model_path = self.download_models(model_id)
+        self.model = yolov9.load(self.model_path, device="cuda:0")
+        
+    # Object behavior / Method -> 3
+    def configure_model(self, conf_threshold, iou_threshold):
+        self.conf_threshold = conf_threshold 
+        self.iou_threshold = iou_threshold 
+        self.model.conf = self.conf_threshold 
+        self.model.iou = self.iou_threshold 
+        
+    # Object behavior / Method -> 4
+    def perform_inference(self, img_path, image_size):
+        self.image_size = image_size
+        results = self.model(img_path, size=self.image_size)
+        output = results.render()
+        return output[0]
+    
+    # Object behavior / Method -> 5 
+    # Note: 5 is a method deriving from within the class with the name 
+    # Inference_Nascent_Spawning_Deriving_From_YOLOv9
+    # One can also declare outside of the OOP as a function, which in turn,
+    # calls the methods inside of the OOP leveraging the functionality
+    # fostering from each unique Object behavior / Method
+    # Personal preferecnce -> This instantiation from within OOP
+    def launch_gradio_app(self):
+        with gr.Blocks() as gradio_app: 
+            with gr.Row():
+                with gr.Column():
+                    img_path = gr.Image(type='filepath', label='Image')
+                    model_id = gr.Dropdown(
+                        label="Model",
+                        choices=[
+                            "gelan-c.pt",
+                            "gelan-e.pt",
+                            "yolov9-c.pt",
+                            "yolov9-e.pt",
+                        ],
+                        value="gelan-e.pt",
+                    )
+                    image_size = gr.Slider(
+                        label="Image Size",
+                        minimum=320,
+                        maximum=1280,
+                        step=32,
+                        value=640, # Default value of 640 foments higher percentage obverse the image detection
+                    )
+                    conf_threshold = gr.Slider(
+                        label="Confidence Threshold",
+                        minimum=0.1,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.4,
+                    )
+                    iou_threshold = gr.Slider(
+                        label="IoU Threshold",
+                        minimum=0.1,
+                        maximum=1.0,
+                        step=0.1,
+                        value=0.5,
+                    )
+                    yolov9_infer = gr.Button(value="Inference")
+                    
+                with gr.Column():
+                    output_numpy = gr.Image(type="numpy", label="Output")
+                    
+            # yolov9_infer leveraging click functionality
+            # Resembles iface = gr.Interface( 
+            #fn=... 
+            #inputs=[], 
+            #outputs=[],
+            yolov9_infer.click(
+                fn=self.perform_inference,
+                inputs=[
+                    img_path,
+                    model_id,
+                    image_size,
+                    conf_threshold,
+                    iou_threshold,
+                ],
+                outputs=[output_numpy],
+            )
+            
+            gr.Examples(
+                examples=[
+                    ["cow.jpeg", "gelan-e.pt", 640, 0.4, 0.5], 
+                    ["techengue_GTA.png", "yolov9-c.pt", 640, 0.4, 0.5],
+                ],
+                fn=self.perform_inference,
+                inputs=[
+                    img_path,
+                    model_id,
+                    image_size,
+                    conf_threshold,
+                    iou_threshold,
+                ].
+                outputs=[output_numpy],
+                cache_examples=True,
+            )
+            
+            gr.HTML(
+                """
+                <h1 style='text-align: center'>
+                YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
+                </h1>
+                """
+            )
+            
+            gr.HTML(
+            """
+            <h3 style='text-align: center'>
+            Expound further notions regarding this topic at: 
+            https://doi.org/10.48550/arXiv.2402.13616
+            """)
+            
+        gradio_app.launch(debug=True)
+        
+# Instantiate the class and launch the Gradio app
+yolo_inference = Inference_Nascent_Spawning_Deriving_From_YOLOv9()
+yolo_inference.launch_gradio_app()