Update app.py

app.py

@@ -1,29 +1,34 @@
-import gradio as gr
-from ultralytics import YOLO
-import os
-import glob
-# Load the trained YOLOv10 model
-model = YOLO('/content/runs/detect/train/weights/best.pt')
-
-# Inference function for Gradio
-def detect_objects(image):
-    # Run prediction
-    results = model.predict(source=image, save=True, conf=0.5)
-
-    # Get the path of the saved image
-    predicted_image_dir = 'runs/detect/predict'
-    predicted_image_path = glob.glob(f"{predicted_image_dir}/*")
-
-    return predicted_image_path[0]
-
-# Create the Gradio interface
-app = gr.Interface(
-    fn=detect_objects,
-    inputs=gr.Image(type="filepath", label="Upload Image"),
-    outputs=gr.Image(type="filepath", label="Detected Image"),
-    title="YOLOv10 Object Detection App",
-    description="Upload an image to detect blood cells (RBC, WBC, Platelets) using the fine-tuned YOLOv10 model."
-)
-
-# Launch the app
-app.launch(share=True)
+import gradio as gr
+from ultralytics import YOLO
+import os
+import glob
+import shutil
+
+# Load the fine-tuned YOLOv10 model
+model = YOLO('best.pt')
+
+# Inference function for Gradio
+def detect_objects(image):
+    # Create a predict directory if it doesn't exist
+    output_dir = "predict"
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    # Run prediction and save results in the output directory
+    results = model.predict(source=image, save=True, project=output_dir, name='results', conf=0.5)
+
+    # Get the latest saved image
+    predicted_image_path = glob.glob(f"{output_dir}/results/*")[-1]
+    return predicted_image_path
+
+# Create the Gradio interface
+app = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="filepath", label="📤 Upload Image"),
+    outputs=gr.Image(type="filepath", label="✅ Detected Image"),
+    title="🔬 YOLOv10 Blood Cell Detection App",
+    description="Upload an image to detect blood cells (RBC, WBC, Platelets) using the fine-tuned YOLOv10 model."
+)
+
+# Launch the app
+app.launch()