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app.py

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+import gradio as gr
+import numpy as np
+import onnxruntime as ort
+import json
+from cryptography.fernet import Fernet
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Model load
+key = os.getenv("ARTER_MODEL_KEY")
+cipher = Fernet(key)
+
+with open("model.bin", "rb") as f:
+    encrypted = f.read()
+    decrypted = cipher.decrypt(encrypted)
+    ort_session = ort.InferenceSession(decrypted)
+
+# Initialize ONNX session
+input_name = ort_session.get_inputs()[0].name
+
+def softmax(x):
+    return np.exp(x) / np.sum(np.exp(x), axis=1)
+
+# Load mappings
+with open("species_to_idx.json", "r") as f:
+    species_to_idx = json.load(f)
+
+with open("idx_to_target.json", "r") as f:
+    idx_to_target = json.load(f)
+    
+target_list = list(idx_to_target.keys())
+
+def predict(species):
+    
+    if not species:
+        return {}, {}, {}
+    
+    input = [species_to_idx[s] for s in species]
+    input_np = np.array(input, dtype=np.int64).reshape(1, -1)
+
+    output = ort_session.run([], {input_name: input_np})
+    output = {target: softmax(output[i]) for i, target in enumerate(target_list)}
+    
+    format_output = []
+    
+    for target in target_list:
+        probs = output[target][0]
+        classes = idx_to_target[target]
+        format_output.append({classes[str(i)]: float(p) for i, p in enumerate(probs)})
+    
+    return format_output
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Naturtype og §3 klassifikation baseret på artsfund")
+    gr.Markdown("Dette værktøj analyserer artsregistreringer for at bestemme et områdes hovednaturtype, naturtilstand og om området er omfattet af Naturbeskyttelseslovens paragraf 3.")
+
+    with gr.Row():
+        choices = sorted(species_to_idx.keys())
+        species_dropdown = gr.Dropdown(
+            choices=choices,
+            multiselect=True,
+            label="Vælg arter",
+        )
+    
+    with gr.Row():
+        naturtype = gr.Label("", label="Hovednaturtype")
+        p3 = gr.Label("", label="§3 område")
+        tilstand = gr.Label("", label="Naturtilstand")
+
+    gr.Markdown("App og model af Kenneth Thorø Martinsen (kenneth2810@gmail.com).")
+
+    species_dropdown.change(
+        predict,
+        inputs=[species_dropdown],
+        outputs=[naturtype, p3, tilstand]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

idx_to_target.json

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+{"hovednaturtype": {"0": "Ferskeng", "1": "Hede", "2": "Ikke en \u00a73 naturtype", "3": "Mose og K\u00e6r", "4": "Overdrev", "5": "Skov", "6": "Strandenge, strandsumpe", "7": "Strandklit", "8": "S\u00f8"}, "arealet_nbl": {"0": "ja", "1": "nej"}, "naturtilstand": {"0": "I", "1": "II", "2": "III", "3": "IV", "4": "V"}}

model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b5d57bf5b46a41a1add8cd98daedf6578ada17d793b5e7c557117c04a9a5a8a
+size 2418872

requirements.txt

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+cryptography
+onnxruntime