Update app.py

app.py

@@ -1,67 +1,42 @@
 import gradio as gr
-import torch
-import cv2
-import numpy as np
-from sahi.prediction import ObjectPrediction
-from sahi.utils.cv import visualize_object_predictions, read_image
-from ultralyticsplus import YOLO
+from ultralytics import YOLO
 
-def yolov8_inference(
-     image: gr.Image = None,
-    model_path: gr.Dropdown = None,
-    image_size: gr.Slider = 640,
-    conf_threshold: gr.Slider = 0.25,
-    iou_threshold: gr.Slider = 0.45,
-):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        model_path: Path to the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
-    model = YOLO(model_path)
-    model.overrides['conf'] = conf_threshold
-    model.overrides['iou']= iou_threshold
-    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
-    image = read_image(image)
-    # Observe results (You should adjust this part based on your result extraction logic)
-    top_class_index = torch.argmax(results[0].probs).item()
-    Class1 = model.names[top_class_index]
+# catgories
+categories =['Good_Tyre','Defective_Tyre']
 
-    return Class1
+# returning classifiers output
 
-# Define Gradio input and output components
+def image_classifier(inp):
+    model = YOLO("best.pt")
 
-inputs = [
-    gr.Image(type="filepath", label="Input Image"),
-    gr.Dropdown(["foduucom/Tyre-Quality-Classification-AI"], 
-                default="foduucom/Tyre-Quality-Classification-AI", label="Model"),
-    gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-outputs = gr.Textbox(label="Result")
+    result = model.predict(source=inp)
+    probs = result[0].probs.data
 
-title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification"
+    # Combine the two lists and sort based on values in descending order
+    sorted_pairs = sorted(zip(categories, probs), key=lambda x: x[1], reverse=True)
 
-description = """
-Welcome to our 🤖 AI-Powered Tire Quality Inspection Space – a cutting-edge solution harnessing the capabilities of YOLOv8s to revolutionize 🚗 tire quality control processes.
-"""
+    resultado = []
+    for name, value in sorted_pairs:
+        resultado.append(f'{name}: {value:.2f}%')
 
-demo_app = gr.Interface(
-    fn=yolov8_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    description=description,
-    theme='huggingface',
-)
+    return ', '.join(resultado)
 
-# Launch the Gradio app
-demo_app.launch()
+# gradio code block for input and output
+with gr.Blocks() as app:
+    gr.Markdown("## Classification for tyre Quality measure (Good tyre and defective tyre) on Yolo-v8")
+    with gr.Row():
+        inp_img = gr.Image()
+        out_txt = gr.Textbox()
+    btn = gr.Button(value="Submeter")
+    btn.click(image_classifier, inputs=inp_img, outputs=out_txt)
+
+    gr.Markdown("## Exemplos")
+    gr.Examples(
+        examples=['Sample/Good tyre.png', 'Sample/Bald tyre.png'],
+        inputs=inp_img,
+        outputs=out_txt,
+        fn=image_classifier,
+        cache_examples=True,
+    )
+
+app.launch(share=True)