Create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+import torch
+from ultralyticsplus import YOLO, render_result
+from PIL import Image
+
+# Function to perform YOLOv8 object detection
+def yoloV8_func(image, image_size=640, conf_threshold=0.4, iou_threshold=0.50):
+    # Load the YOLOv8 model from the 'best.pt' checkpoint
+    model_path = "best.pt"
+    model = YOLO(model_path)
+
+    # Perform object detection on the input image using the YOLOv8 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+    # Render the output image with bounding boxes around detected objects
+    render = render_result(model=model, image=image, result=results[0])
+    return render
+
+# Streamlit app
+def main():
+    st.title("YOLOv8 Object Detection")
+    st.write("Upload an image and set the parameters to detect objects.")
+
+    # Upload image
+    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    # Parameters
+    image_size = st.slider("Image Size", min_value=320, max_value=1280, value=640, step=32)
+    conf_threshold = st.slider("Confidence Threshold", min_value=0.0, max_value=1.0, value=0.4, step=0.05)
+    iou_threshold = st.slider("IOU Threshold", min_value=0.0, max_value=1.0, value=0.50, step=0.05)
+
+    # Perform object detection when an image is uploaded
+    if uploaded_image is not None:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.write("")
+        st.write("Detecting objects...")
+
+        # Perform object detection
+        output_image = yoloV8_func(image, image_size, conf_threshold, iou_threshold)
+        st.image(output_image, caption="Output Image", use_column_width=True)
+
+if __name__ == "__main__":
+    main()