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app.py

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+import gradio as gr
+import sys
+sys.path.append('./utils')
+
+from yolo_utils import preprocess_image_pil, run_model, process_results, plot_results_gradio
+import matplotlib.pyplot as plt
+import io
+from ultralytics import YOLO
+
+def process_image(image,conf,iou):
+    model = YOLO('./trained_models/nano.pt')
+    # Preprocess the image
+    preprocessed_image = preprocess_image_pil(image, threshold_value=0.9, upscale=False)
+
+    # Run the model
+    results = run_model(model, preprocessed_image, conf=conf, iou=iou, imgsz=640)
+
+    # Process the results
+    input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss = process_results(results, preprocessed_image)
+
+    # Plot the results
+    fig = plot_results_gradio(input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss)
+
+    # Convert the plot to an image
+
+    return fig
+
+# Create the Gradio interface
+title = "YOLOV8-TO Demo App"
+description = "Upload an image and see the processed results. Adjust the confidence and IOU thresholds as needed."
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type='pil'),
+        gr.Slider(minimum=0, maximum=1, value=0.1, label="Confidence Threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.5, label="IOU Threshold")
+    ],
+    outputs="image",
+    title=title,
+    description=description
+)
+
+iface.launch()