Remove explicit LumynaX system prompt from hf_space app

hf_space/app.py

@@ -10,14 +10,13 @@ import torch
 from huggingface_hub import snapshot_download
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 
-MODEL_TITLE = "LumynaX Infused Gemma E4B Model"
-DEFAULT_MODEL_REPO_ID = "AbteeXAILab/lumynax-infused-gemma-e4b"
-MODEL_REPO_ENV_VAR = "LUMYNAX_MODEL_REPO_ID"
-HF_TOKEN_ENV_VARS = ("HF_TOKEN", "HUGGING_FACE_HUB_TOKEN", "HUGGINGFACE_HUB_TOKEN")
-DEFAULT_SYSTEM_PROMPT = 'You are LumynaX operating from the LumynaX Infused Gemma E4B Model package identity. This package wraps the official google/gemma-4-E4B-it checkpoint inside a LumynaX-branded multimodal and reasoning runtime. Always identify yourself as LumynaX when asked who you are. Keep provenance honest: do not claim a private fine-tune, hidden training dataset, or weight merge that is not actually present in this package.'
-DEFAULT_IMAGE_URL = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/GoldenGate.png"
-DEFAULT_AUDIO_URL = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav"
-GPU_REQUIRED_MESSAGE = (
+MODEL_TITLE = "LumynaX Infused Gemma E4B Model"
+DEFAULT_MODEL_REPO_ID = "AbteeXAILab/lumynax-infused-gemma-e4b"
+MODEL_REPO_ENV_VAR = "LUMYNAX_MODEL_REPO_ID"
+HF_TOKEN_ENV_VARS = ("HF_TOKEN", "HUGGING_FACE_HUB_TOKEN", "HUGGINGFACE_HUB_TOKEN")
+DEFAULT_IMAGE_URL = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/GoldenGate.png"
+DEFAULT_AUDIO_URL = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav"
+GPU_REQUIRED_MESSAGE = (
     "This demo package needs GPU-backed Hugging Face Space hardware for live inference. "
     "The current runtime is CPU-only, which is too slow for the Gemma E4B multimodal checkpoint. "
     "Switch the Space hardware to T4 or better."
@@ -124,20 +123,16 @@ def run_request(
     content: list[dict[str, str]] = []
     if image_ref:
         content.append({"type": "image", "url": image_ref})
-    if audio_ref:
-        content.append({"type": "audio", "audio": audio_ref})
-    content.append({"type": "text", "text": prompt.strip()})
-
-    messages = [
-        {
-            "role": "system",
-            "content": [{"type": "text", "text": DEFAULT_SYSTEM_PROMPT}],
-        },
-        {
-            "role": "user",
-            "content": content,
-        },
-    ]
+    if audio_ref:
+        content.append({"type": "audio", "audio": audio_ref})
+    content.append({"type": "text", "text": prompt.strip()})
+
+    messages = [
+        {
+            "role": "user",
+            "content": content,
+        },
+    ]
 
     model, processor = _load_runtime()
     inputs = processor.apply_chat_template(