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README.md

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 ---
 title: KInIT Multilingual Machine-Generated Text Detector
-emoji: 📉
-colorFrom: red
-colorTo: red
+emoji: ⚡
+colorFrom: indigo
+colorTo: pink
 sdk: gradio
 sdk_version: 4.44.0
 app_file: app.py

app.py

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+import os
+import gradio as gr
+import requests
+import json
+from urllib.parse import quote
+
+auth_token = os.environ.get("kinit_mgt_access_token")
+share = os.environ.get("GRADIO_SHARE")
+
+def get_api_response(text):
+   url = "https://mgt-detector.model.kinit.sk/prod/?q=" + quote(text)
+   payload = {}
+   headers = {
+       'x-api-key': auth_token
+   }
+   response = requests.request("GET", url, headers=headers, data=payload)
+   response = json.loads(response.text)
+   return response
+
+def predict(text):
+    #return 'machine', 1.0
+    res = get_api_response(text)
+    if 'pred' not in res.keys():
+        return "Waiting for the server startup, try again!", "Waiting for the server startup, try again!"
+    pred = "Very likely human-written"
+    if res['score'] > 0.05: pred = "Likely human-written"
+    if res['score'] > 0.5: pred = "Likely machine-generated"
+    if res['score'] > 0.95: pred = "Very likely machine-generated"
+    return pred,res['score']
+
+with gr.Blocks(analytics_enabled=False) as demo:
+    gr.Markdown("""
+## KInIT Multilingual Machine-Generated Text Detector                
+Trained on [MULTITuDE](https://aclanthology.org/2023.emnlp-main.616/) (news articles) and [MultiSocial](https://arxiv.org/abs/2406.12549) (social media texts) texts in 22 languages. 
+                """)
+    gr.Markdown("""
+**Disclaimer: The detector is based on AI transformer model and is NOT 100% accurate! Usage is intended for research purpose only, as an indicator. Do not use it for direct decision making!**""")
+    gr.Markdown("""To generate examplar text by a large language model, you can use [HuggingFace Chat](https://huggingface.co/chat/).""")
+    t1 = gr.Textbox(lines=10, label='Text',value="Put your text (in any language) in here to try out our multilingual machine-generated text detector.")
+    button1 = gr.Button("Run detection")
+    label1 = gr.Textbox(lines=1, label='Result')
+    score1 = gr.Textbox(lines=1, label='Probability (closer to 0 means higher probability of text being written by a human, closer to 1 means higher probability of text being generated by an AI model)')
+    
+    button1.click(predict, inputs=[t1], outputs=[label1,score1], api_name=False)
+
+if __name__ == "__main__":
+    demo.launch(show_api=False, share=share)

requirements.txt

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+gradio
+requests