added

app.py

@@ -0,0 +1,36 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from model import load_model, predict_species, get_label_names
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+app = Flask(__name__)
+
+# Configure CORS with environment variables
+cors_origins = os.getenv('CORS_ORIGINS', 'http://localhost:3000').split(',')
+CORS(app, origins=cors_origins)
+
+model = load_model()
+label_names = get_label_names()
+
+@app.route('/predict', methods=['GET'])
+def predict():
+    image_url = request.args.get('url')
+    if not image_url:
+        return jsonify({'error': 'URL parameter is missing'}), 400
+    try:
+        predicted_species = predict_species(model, image_url, label_names)
+        return jsonify({'species': predicted_species})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    # Get configuration from environment variables
+    host = os.getenv('FLASK_HOST', '127.0.0.1')
+    port = int(os.getenv('FLASK_PORT', 5000))
+    debug = os.getenv('FLASK_DEBUG', 'True').lower() == 'true'
+    
+    app.run(host=host, port=port, debug=debug)

model.py

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+import timm
+import torch
+from PIL import Image
+from torchvision import transforms
+import requests
+from io import BytesIO
+
+
+def load_model():
+    """Load the pre-trained model."""
+    model = timm.create_model("hf_hub:timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k_inat21", pretrained=True)
+    model.eval()
+    return model
+
+
+def get_label_names():
+    """Fetch the class labels from the Hugging Face Hub."""
+    config_url = "https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k_inat21/resolve/main/config.json"
+    response = requests.get(config_url)
+    response.raise_for_status()
+    config = response.json()
+    return config["label_names"]
+
+
+def preprocess_image(image_url):
+    """Fetch and preprocess the image."""
+    preprocess = transforms.Compose([
+        transforms.Resize(336),
+        transforms.CenterCrop(336),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+
+    response = requests.get(image_url)
+    response.raise_for_status()
+    image = Image.open(BytesIO(response.content))
+    input_tensor = preprocess(image).unsqueeze(0)  # Add a batch dimension
+    return input_tensor
+
+
+def predict_species(model, image_url, label_names):
+    """Make a prediction using the model."""
+    input_tensor = preprocess_image(image_url)
+
+    # Move to GPU if available
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+    input_tensor = input_tensor.to(device)
+
+    # Make prediction
+    with torch.no_grad():
+        output = model(input_tensor)
+        _, predicted_class = torch.max(output, 1)
+
+    # Map prediction to species
+    predicted_species = label_names[predicted_class.item()]
+    return predicted_species
+#finish

requirements.txt

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+flask==2.3.3
+flask-cors==4.0.0
+timm==0.9.5
+torch==2.8.0
+torchvision==0.23.0
+pillow==10.0.0
+requests==2.31.0
+gunicorn
+python-dotenv==1.0.0