Update app.py

app.py

@@ -1,16 +1,90 @@
 import gradio as gr
-import torch
 from ultralytics import YOLO
 
-# Load YOLO model
-model = YOLO("yolov9c-seg.pt")
+# Define function to perform prediction with YOLOv9 model
+def predict_image(image, model_path, image_size, conf_threshold, iou_threshold):
+    # Load YOLO model
+    model = YOLO(model_path)
+    
+    # Perform inference with YOLO model
+    results = model(image, size=image_size, conf=conf_threshold, iou=iou_threshold)
+    
+    # Render the output
+    output_image = results.render()
+    
+    return output_image[0]
 
-# Define function to perform prediction
-def predict_image(image):
-    result = model.predict(image, show=True, save=True)
-    return result
+# Define Gradio interface
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                img_path = gr.Image(type="filepath", label="Image")
+                model_path = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "yolov9c-seg.pt",
+                        "yolov5m.pt",
+                        "yolov5l.pt",
+                        "yolov5x.pt",                                              
+                    ],
+                    value="yolov5s.pt",
+                )
+                image_size = gr.Slider(
+                    label="Image Size",
+                    minimum=320,
+                    maximum=1280,
+                    step=32,
+                    value=640,
+                )
+                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="Submit")
+
+            with gr.Column():
+                output_numpy = gr.Image(type="numpy", label="Output")
+
+        yolov9_infer.click(
+            fn=predict_image,
+            inputs=[
+                img_path,
+                model_path,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_numpy],
+        )   
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    YOLOv9 Base Model
+    </h1>
+    """)
+    gr.HTML(
+        """
+        <h3 style='text-align: center'>
+        </h3>
+        """)
+    with gr.Row():
+        with gr.Column():
+            app()    
+    
+gradio_app.launch(debug=True)
 
-# Create Gradio interface
-image_input = gr.inputs.Image()
-gr.Interface(predict_image, image_input, "image").launch()