Create inference.py

inference.py

@@ -0,0 +1,39 @@
+import os
+import gradio as gr
+
+# Environment variables – NO default for HF_TOKEN (must be None if missing)
+API_BASE_URL = os.getenv("API_BASE_URL")        # can have a default if you want, but the instruction says "defaults are set only for API_BASE_URL and MODEL_NAME"
+MODEL_NAME = os.getenv("MODEL_NAME")
+HF_TOKEN = os.getenv("HF_TOKEN")                # MUST NOT have a default (no `or "something"`)
+
+# Optional – only if you use from_docker_image()
+LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
+
+# Optional: If you need to use OpenAI client, configure it like:
+# from openai import OpenAI
+# client = OpenAI(api_key=HF_TOKEN, base_url=API_BASE_URL)
+
+def predict(packet_data: str):
+    """
+    Replace this with your actual MayOne detection logic.
+    You can import your existing framework modules if you copy them into the Space.
+    """
+    # Dummy result – replace with real inference
+    result = {
+        "anomaly_score": 0.92,
+        "verdict": "malicious",
+        "model": MODEL_NAME or "MayOne-Security-Framework"
+    }
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Packet Data (hex or raw)", lines=5),
+    outputs=gr.JSON(label="Detection Result"),
+    title="MayOne Security Framework",
+    description="AI-powered network intrusion detection for Windows"
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)