app.py

app.py

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+import gradio as gr
+import torch
+import pickle
+from torchvision import transforms
+from torchvision.transforms import ToTensor, RandomErasing
+from PIL import Image
+import numpy as np
+from ultralytics import YOLO 
+import io
+
+
+# Load the YOLO model for bird detection
+yolo_model = YOLO('yolov5su.pt')
+
+class CPU_Unpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        if module == 'torch.storage' and name == '_load_from_bytes':
+            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')  # Ensure loading on CPU
+        else:
+            return super().find_class(module, name)
+
+# Load your model using the custom unpickler
+with open(".\model\model_resultsconvnext_large.pkl", "rb") as file:
+    model = CPU_Unpickler(file).load()
+    model = model['convnext_large']['model']
+model.eval()
+# Function to detect bird region
+def detect_bird_region(image):
+    results = yolo_model(image, verbose=False)
+    bird_boxes = results[0].boxes[results[0].boxes.cls == 14] 
+    if len(bird_boxes) > 0:
+        return bird_boxes[0].xyxy[0].cpu().numpy()  # Coordinates of the first detected bird
+    return None
+
+# Preprocessing function for inference
+def preprocess_image(image):
+    bird_box = detect_bird_region(image)
+    if bird_box is not None:
+        image = image.crop(bird_box)  # Crop to bird region
+
+    # Apply validation transformations
+    val_transform = transforms.Compose([
+        transforms.Resize((229, 229)),
+        ToTensor(),
+        transforms.ConvertImageDtype(torch.float32),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    return val_transform(image).unsqueeze(0)  # Add batch dimension
+
+# Prediction function
+def predict(image):
+    # Preprocess the image
+    image = preprocess_image(image)
+
+    # Perform prediction
+    with torch.no_grad():
+        outputs = model(image)
+        predicted_class = torch.argmax(outputs, dim=1).item()
+    
+    # Map the predicted class to bird names using bird_folders
+    bird_folders = {
+        0: "019.Gray_Catbird",
+        1: "025.Pelagic_Cormorant",
+        2: "026.Bronzed_Cowbird",
+        3: "029.American_Crow",
+        4: "039.Least_Flycatcher",
+        5: "073.Blue_Jay",
+        6: "085.Horned_Lark",
+        7: "099.Ovenbird",
+        8: "104.American_Pipit",
+        9: "119.Field_Sparrow",
+        10: "127.Savannah_Sparrow",
+        11: "129.Song_Sparrow",
+        12: "135.Bank_Swallow",
+        13: "137.Cliff_Swallow",
+        14: "138.Tree_Swallow",
+        15: "142.Black_Tern",
+        16: "143.Caspian_Tern",
+        17: "144.Common_Tern",
+        18: "167.Hooded_Warbler",
+        19: "176.Prairie_Warbler",
+        20: "177.Prothonotary_Warbler",
+        21: "179.Tennessee_Warbler",
+        22: "182.Yellow_Warbler",
+        23: "183.Northern_Waterthrush",
+        24: "185.Bohemian_Waxwing",
+        25: "186.Cedar_Waxwing",
+        26: "188.Pileated_Woodpecker",
+        27: "192.Downy_Woodpecker",
+        28: "195.Carolina_Wren",
+        29: "199.Winter_Wren"
+    }
+    return bird_folders[predicted_class]  # Return bird name as output
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="label"  # Display class label as output
+)
+
+# Launch Gradio App
+interface.launch()