Create app.py

app.py

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+import streamlit as st
+from ultralyticsplus import YOLO, render_result
+import PIL.Image as Image
+import numpy as np
+import requests
+from io import BytesIO
+
+# Initialize Streamlit app
+st.title("Blood Cell Detection with YOLOv8")
+
+# Load YOLO model
+model = YOLO('keremberke/yolov8m-blood-cell-detection')
+
+# Set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45   # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # Maximum number of detections per image
+
+# File uploader for image input
+uploaded_file = st.file_uploader("Upload an image for detection", type=["jpg", "png"])
+
+if uploaded_file:
+    # Open the uploaded image
+    image = Image.open(uploaded_file)
+    
+    # Perform inference
+    results = model.predict(np.array(image))
+    
+    # Display results
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Render detection results
+    rendered_image = render_result(model=model, image=image, result=results[0])
+    
+    # Show the rendered result
+    st.image(rendered_image, caption="Detection Results", use_column_width=True)
+
+    # Display details of detected boxes
+    st.write("Detection Results:")
+    for box in results[0].boxes:
+        st.write(f"Bounding box: {box.xyxy}")
+        st.write(f"Confidence: {box.conf}")
+        st.write(f"Class: {box.cls}")
+
+else:
+    st.write("Upload an image to start detection")