Update app.py

app.py

@@ -1,48 +1,48 @@
-import gradio as gr
-from ultralytics import YOLO
-from PIL import Image
-import numpy as np
-
-# Load YOLO model
-model = YOLO("best.pt")
-
-# Define the prediction function
-def detect_species(files):
-    """
-    Detect species in uploaded images using the YOLO model.
-    
-    Args:
-        files (list): List of uploaded image files.
-    
-    Returns:
-        list: Annotated images or error messages for each file.
-    """
-    annotated_images = []
-    for file in files:  # Loop through the list of uploaded files
-        try:
-            # Open and process the image
-            image = Image.open(file)
-            image = np.array(image)  # Convert to numpy array
-            results = model(image)  # Run the YOLO model
-            annotated_image = results[0].plot()  # Generate annotated image
-            annotated_images.append(annotated_image)  # Add to results
-        except Exception as e:
-            # Handle errors (e.g., unsupported or corrupt files)
-            error_message = f"Error processing file {file.name}: {str(e)}"
-            print(error_message)  # Log the error
-            annotated_images.append(np.zeros((100, 100, 3)))  # Placeholder image
-    return annotated_images
-
-# Create the Gradio app
-app = gr.Interface(
-    fn=detect_species,
-    inputs=gr.Files(file_types=["image"], label="Upload Images"),  # Allow multiple image uploads
-    outputs=gr.Gallery(label="Detection Results"),  # Display results in a gallery
-    title="Plant Species Detector",
-    description="Upload one or more leaf images, and the model will detect the species.",
-    examples=["example1.jpg", "example2.jpg"],  # Optional: Add example images
-)
-
-# Launch the app
-if __name__ == "__main__":
-    app.launch(share=True)  # Set share=True to get a public URL
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+
+# Load YOLO model
+model = YOLO("best.pt")
+
+# Define the prediction function
+def detect_species(files):
+    """
+    Detect species in uploaded images using the YOLO model.
+    
+    Args:
+        files (list): List of uploaded image files.
+    
+    Returns:
+        list: Annotated images or error messages for each file.
+    """
+    annotated_images = []
+    for file in files:  # Loop through the list of uploaded files
+        try:
+            # Open and process the image
+            image = Image.open(file)
+            image = np.array(image)  # Convert to numpy array
+            results = model(image)  # Run the YOLO model
+            annotated_image = results[0].plot()  # Generate annotated image
+            annotated_images.append(annotated_image)  # Add to results
+        except Exception as e:
+            # Handle errors (e.g., unsupported or corrupt files)
+            error_message = f"Error processing file {file.name}: {str(e)}"
+            print(error_message)  # Log the error
+            annotated_images.append(np.zeros((100, 100, 3)))  # Placeholder image
+    return annotated_images
+
+# Create the Gradio app
+app = gr.Interface(
+    fn=detect_species,
+    inputs=gr.Files(file_types=["image"], label="Upload Images"),  # Allow multiple image uploads
+    outputs=gr.Gallery(label="Detection Results"),  # Display results in a gallery
+    title="Finnish Meadow Plants Detection",
+    description="Upload one or more leaf images, and the model will detect the species.",
+    examples=["example1.jpg", "example2.jpg"],  # Optional: Add example images
+)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch(share=True)  # Set share=True to get a public URL