[Model]Support MiniCPM-o 4.5 (#3642)

Signed-off-by: tc-mb <tianchi_cai@icloud.com>
Co-authored-by: GKangaroo <1095103651@qq.com>
Co-authored-by: GKangaroo <gqx24@mails.tsinghua.edu.cn>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

examples/online_serving/minicpmo/README.md

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+# vLLM-Omni · MiniCPM-o 4.5 Online Demo
+
+Gradio-based web UI for **MiniCPM-o 4.5** served via `vllm-omni`'s
+OpenAI-compatible endpoints.
+
+The UI supports:
+
+- **Inputs**: text prompt + optional image, audio (file or mic), video.
+- **Outputs**: text + speech (WAV player).
+
+## 1. Start the backend server
+
+The deploy config auto-loads via `--omni`; the default
+`vllm_omni/deploy/minicpmo_4_5.yaml` targets a 2-GPU layout (thinker on
+GPU 0, talker + t2w sharing GPU 1).  For other hardware layouts pick
+one of the deploy variants below.
+
+| deploy config | GPUs | Notes |
+|---|---|---|
+| `minicpmo_4_5.yaml` (default) | 2 | Thinker on GPU0, talker+t2w on GPU1. |
+| `minicpmo_4_5_3gpu.yaml` | 3 | Thinker 2-way TP on GPU0/1, talker+t2w share GPU2. |
+| `minicpmo_4_5_8x4090.yaml` | 8 | Full 8x4090 layout. |
+
+Default (2-GPU):
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni \
+    --trust-remote-code \
+    --host 0.0.0.0 --port 8099
+```
+
+Other layouts via `--deploy-config`:
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni \
+    --deploy-config vllm_omni/deploy/minicpmo_4_5_8x4090.yaml \
+    --trust-remote-code \
+    --host 0.0.0.0 --port 8099
+```
+
+## 2. Launch the Gradio demo
+
+```bash
+bash examples/online_serving/minicpmo/run_gradio_demo.sh
+
+# Or run the python entry point directly:
+python examples/online_serving/minicpmo/gradio_demo.py \
+    --minicpmo45-api-base http://localhost:8099/v1 \
+    --minicpmo45-model openbmb/MiniCPM-o-4_5 \
+    --port 7862
+```
+
+Open `http://<host>:7862` in a browser.
+
+## Notes
+
+- **TTS trigger**: the demo sets
+  `extra_body.chat_template_kwargs.use_tts_template=True`, which appends
+  `<|tts_bos|>` to the assistant prefix.
+- Uncheck **"Generate speech output (TTS)"** to get text-only responses
+  (faster).
+- The audio output is the raw WAV returned by the stage-1 talker +
+  Token2Wav; sample rate is 24 kHz.
+- Video input is forwarded as a base64 `video_url` entry; the server needs
+  decord/torchvision to decode it.

examples/online_serving/minicpmo/gradio_demo.py

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+"""Gradio demo for MiniCPM-o 4.5 online serving through vllm-omni.
+
+Supports:
+- Text / Image / Audio / Video inputs (OpenAI chat-completions multimodal format).
+- Text + audio outputs, returned side-by-side in the UI.
+
+Usage:
+    # run a 4.5 server on :8099
+    python gradio_demo.py \
+        --minicpmo45-api-base http://localhost:8099/v1 \
+        --minicpmo45-model openbmb/MiniCPM-o-4_5 \
+        --port 7862
+"""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import io
+import os
+from collections.abc import Iterable
+from pathlib import Path
+from typing import Any
+
+import gradio as gr
+import numpy as np
+import soundfile as sf
+from openai import OpenAI
+from PIL import Image
+
+MINICPMO45 = "MiniCPM-o-4.5"
+
+# Default reference audio per model, relative to the model path. Used to build
+# the audio_assistant system prompt so that the LLM adopts the "speak in this
+# voice" persona (matches the official MiniCPM-o demo).
+_DEFAULT_REF_AUDIO = {
+    MINICPMO45: "assets/HT_ref_audio.wav",
+}
+
+# audio_assistant prompt text, mirrors MiniCPMO.get_sys_prompt("audio_assistant")
+# in the MiniCPM-o-Demo reference implementation.
+_AUDIO_ASSISTANT_PROMPT = {
+    "zh": {
+        "prefix": "模仿音频样本的音色并生成新的内容。",
+        "suffix": (
+            "你的任务是用这种声音模式来当一个助手。请认真、高质量地回复用户的问题。"
+            "请用高自然度的方式和用户聊天。你是由面壁智能开发的人工智能助手：面壁小钢炮。"
+        ),
+    },
+    "en": {
+        "prefix": "Use the voice in the audio prompt to synthesize new content.",
+        "suffix": "You are a helpful assistant with the above voice style.",
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Media helpers
+# ---------------------------------------------------------------------------
+
+
+def image_to_base64_data_url(image: Image.Image) -> str:
+    buf = io.BytesIO()
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    image.save(buf, format="JPEG", quality=92)
+    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+    return f"data:image/jpeg;base64,{b64}"
+
+
+def audio_to_base64_data_url(audio_np: np.ndarray, sample_rate: int) -> str:
+    if audio_np.dtype != np.int16:
+        if audio_np.dtype in (np.float32, np.float64):
+            audio_np = np.clip(audio_np, -1.0, 1.0)
+            audio_np = (audio_np * 32767).astype(np.int16)
+        else:
+            audio_np = audio_np.astype(np.int16)
+    buf = io.BytesIO()
+    sf.write(buf, audio_np, sample_rate, format="WAV")
+    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+    return f"data:audio/wav;base64,{b64}"
+
+
+def ref_audio_to_data_url(path: str, target_sr: int = 16000, max_s: float | None = 8.0) -> str:
+    """Load a file, downmix to mono, resample to 16kHz, optionally truncate, and
+    return a data-URL usable as audio_url content in the chat API.
+
+    MiniCPM-o expects 16kHz mono audio input; long reference audios can waste
+    tokens so we cap at max_s seconds by default (3-10s is the recommended range).
+    """
+    data, sr = sf.read(path)
+    if data.ndim > 1:
+        data = data[:, 0]
+    data = data.astype(np.float32)
+    if sr != target_sr:
+        try:
+            # Demo client only; librosa here resamples audio before sending to
+            # the server.  The fallback below covers environments without it.
+            import librosa  # noqa: TID251
+
+            data = librosa.resample(data, orig_sr=sr, target_sr=target_sr)
+        except ImportError:
+            from math import gcd
+
+            g = gcd(sr, target_sr)
+            up, down = target_sr // g, sr // g
+            idx = (np.arange(len(data) * up) / up).astype(np.int64)
+            data = data[idx][::down]
+        sr = target_sr
+    if max_s is not None and len(data) > int(max_s * sr):
+        data = data[: int(max_s * sr)]
+    return audio_to_base64_data_url(data, sr)
+
+
+def video_to_base64_data_url(path: str) -> str:
+    p = Path(path)
+    if not p.exists():
+        raise FileNotFoundError(f"Video not found: {path}")
+    ext = p.suffix.lower()
+    mime = {
+        ".mp4": "video/mp4",
+        ".webm": "video/webm",
+        ".mov": "video/quicktime",
+        ".avi": "video/x-msvideo",
+        ".mkv": "video/x-matroska",
+    }.get(ext, "video/mp4")
+    b64 = base64.b64encode(p.read_bytes()).decode("utf-8")
+    return f"data:{mime};base64,{b64}"
+
+
+def process_audio_input(audio_input: Any | None) -> tuple[np.ndarray, int] | None:
+    """Normalize Gradio audio input to (np.ndarray mono float32, sample_rate).
+
+    Gradio's `gr.Audio(type="numpy")` yields (sr, ndarray); we also accept
+    filesystem paths and a few nested tuple variants that appear in different
+    Gradio versions.
+    """
+    if audio_input is None:
+        return None
+
+    def _from_path(p: str) -> tuple[np.ndarray, int] | None:
+        if not p:
+            return None
+        fp = Path(p)
+        if not fp.exists():
+            return None
+        data, sr = sf.read(fp)
+        if data.ndim > 1:
+            data = data[:, 0]
+        return data.astype(np.float32), int(sr)
+
+    audio_np: np.ndarray | None = None
+    sr: int | None = None
+
+    if isinstance(audio_input, tuple) and len(audio_input) == 2:
+        a, b = audio_input
+        if isinstance(a, int | float) and isinstance(b, np.ndarray):
+            sr, audio_np = int(a), b
+        elif isinstance(a, str):
+            loaded = _from_path(a)
+            if loaded is not None:
+                audio_np, sr = loaded
+    elif isinstance(audio_input, str):
+        loaded = _from_path(audio_input)
+        if loaded is not None:
+            audio_np, sr = loaded
+
+    if audio_np is None or sr is None:
+        return None
+    if audio_np.ndim > 1:
+        audio_np = audio_np[:, 0]
+    return audio_np.astype(np.float32), sr
+
+
+# ---------------------------------------------------------------------------
+# Per-model inference config
+# ---------------------------------------------------------------------------
+
+
+class ModelEndpoint:
+    """Endpoint + TTS-trigger convention for one MiniCPM-o variant."""
+
+    def __init__(self, name: str, api_base: str, model_path: str):
+        self.name = name
+        self.api_base = api_base
+        self.model_path = model_path
+        self.client = OpenAI(api_key="EMPTY", base_url=api_base)
+        self._ref_audio_data_url: str | None = None
+
+    @property
+    def ref_audio_path(self) -> str | None:
+        rel = _DEFAULT_REF_AUDIO.get(self.name)
+        if not rel:
+            return None
+        full = os.path.join(self.model_path, rel)
+        return full if os.path.exists(full) else None
+
+    def get_ref_audio_data_url(self) -> str | None:
+        """Lazily load and cache the default reference audio as a 16kHz mono
+        data URL. Returns None if the reference audio file is missing."""
+        if self._ref_audio_data_url is not None:
+            return self._ref_audio_data_url
+        p = self.ref_audio_path
+        if not p:
+            print(f"[{self.name}] no default reference audio found; falling back to text-only system prompt")
+            return None
+        try:
+            self._ref_audio_data_url = ref_audio_to_data_url(p)
+            print(f"[{self.name}] loaded reference audio: {p}")
+        except Exception as e:
+            print(f"[{self.name}] failed to load reference audio {p}: {e}")
+            return None
+        return self._ref_audio_data_url
+
+    def build_extras(self) -> dict[str, Any]:
+        """Extra kwargs passed to chat.completions.create for this model.
+
+        4.5: use_tts_template=True chat-template flag appends <|tts_bos|>.
+        """
+        if self.name == MINICPMO45:
+            return {
+                "extra_body": {
+                    "chat_template_kwargs": {"use_tts_template": True},
+                    "modalities": ["text", "audio"],
+                },
+            }
+        return {}
+
+
+# ---------------------------------------------------------------------------
+# System prompt (matches MiniCPMO.get_sys_prompt("audio_assistant") in the
+# official MiniCPM-o-Demo). When TTS is disabled we fall back to a plain
+# text-only system prompt.
+# ---------------------------------------------------------------------------
+
+
+def build_audio_assistant_system(
+    ref_audio_data_url: str | None,
+    language: str = "zh",
+) -> dict[str, Any]:
+    """Return an OpenAI-style system message carrying the reference audio."""
+    txt = _AUDIO_ASSISTANT_PROMPT.get(language, _AUDIO_ASSISTANT_PROMPT["zh"])
+    if ref_audio_data_url is None:
+        return {
+            "role": "system",
+            "content": (
+                "You are MiniCPM-o, a helpful multimodal assistant that can "
+                "understand images, audio and video, and respond in text and speech."
+            ),
+        }
+    return {
+        "role": "system",
+        "content": [
+            {"type": "text", "text": txt["prefix"]},
+            {"type": "audio_url", "audio_url": {"url": ref_audio_data_url}},
+            {"type": "text", "text": txt["suffix"]},
+        ],
+    }
+
+
+# ---------------------------------------------------------------------------
+# Core inference function
+# ---------------------------------------------------------------------------
+
+
+def run_inference(
+    endpoint: ModelEndpoint,
+    user_prompt: str,
+    audio_tuple: tuple[np.ndarray, int] | None,
+    image_pil: Image.Image | None,
+    video_path: str | None,
+    enable_tts: bool,
+    max_tokens: int,
+    temperature: float,
+) -> Iterable[tuple[str, tuple[int, np.ndarray] | None]]:
+    user_prompt = user_prompt or ""
+    if not user_prompt.strip() and not audio_tuple and not image_pil and not video_path:
+        yield "Please provide a text prompt or at least one multimodal input.", None
+        return
+
+    try:
+        content: list[dict[str, Any]] = []
+
+        if audio_tuple is not None:
+            audio_np, sr = audio_tuple
+            content.append(
+                {"type": "audio_url", "audio_url": {"url": audio_to_base64_data_url(audio_np, sr)}},
+            )
+
+        if image_pil is not None:
+            img = image_pil if image_pil.mode == "RGB" else image_pil.convert("RGB")
+            content.append(
+                {"type": "image_url", "image_url": {"url": image_to_base64_data_url(img)}},
+            )
+
+        if video_path:
+            content.append(
+                {"type": "video_url", "video_url": {"url": video_to_base64_data_url(video_path)}},
+            )
+
+        if user_prompt.strip():
+            content.append({"type": "text", "text": user_prompt})
+
+        if enable_tts:
+            ref_url = endpoint.get_ref_audio_data_url()
+            system_msg = build_audio_assistant_system(ref_url, language="zh")
+        else:
+            system_msg = {
+                "role": "system",
+                "content": (
+                    "You are MiniCPM-o, a helpful multimodal assistant that can "
+                    "understand images, audio and video, and respond in text and speech."
+                ),
+            }
+
+        messages = [
+            system_msg,
+            {"role": "user", "content": content},
+        ]
+
+        kwargs: dict[str, Any] = {
+            "model": endpoint.model_path,
+            "messages": messages,
+            "max_tokens": int(max_tokens),
+            "temperature": float(temperature),
+        }
+        if enable_tts:
+            extras = endpoint.build_extras()
+            # Merge extra_body dicts cleanly.
+            extra_body = dict(extras.get("extra_body", {}))
+            kwargs["extra_body"] = extra_body
+        else:
+            # Text-only: suppress audio output.
+            kwargs["extra_body"] = {"modalities": ["text"]}
+
+        completion = endpoint.client.chat.completions.create(**kwargs)
+
+        text_parts: list[str] = []
+        audio_out: tuple[int, np.ndarray] | None = None
+
+        for choice in completion.choices:
+            msg = choice.message
+            if getattr(msg, "content", None):
+                text_parts.append(msg.content)
+            audio_obj = getattr(msg, "audio", None)
+            if audio_obj and getattr(audio_obj, "data", None):
+                raw = base64.b64decode(audio_obj.data)
+                if len(raw) > 80:
+                    data, sr = sf.read(io.BytesIO(raw))
+                    if data.ndim > 1:
+                        data = data[:, 0]
+                    audio_out = (int(sr), data.astype(np.float32))
+
+        text_response = "\n\n".join(t for t in text_parts if t) or "(no text returned)"
+        yield text_response, audio_out
+    except Exception as exc:  # noqa: BLE001
+        yield f"Inference failed: {type(exc).__name__}: {exc}", None
+
+
+# ---------------------------------------------------------------------------
+# UI
+# ---------------------------------------------------------------------------
+
+DEFAULT_PROMPTS = [
+    "你好，请用一句话介绍北京。",
+    "Describe what you see in the image.",
+    "Transcribe the audio and translate it into English.",
+    "What is happening in this video?",
+]
+
+
+def build_interface(endpoints: dict[str, ModelEndpoint], default_model: str) -> gr.Blocks:
+    model_choices = list(endpoints.keys())
+
+    css = """
+    #generate-btn button { width: 100%; font-size: 1.05rem; }
+    .media-row > div { min-height: 260px; }
+    .model-banner { color: #555; font-size: 0.9rem; }
+    """
+
+    with gr.Blocks(css=css, title="vLLM-Omni · MiniCPM-o Demo") as demo:
+        gr.Markdown(
+            "# vLLM-Omni · MiniCPM-o Online Demo\n"
+            "Multimodal chat (text / image / audio / video in; text + speech out) for "
+            "MiniCPM-o 4.5 served by vllm-omni."
+        )
+
+        with gr.Row():
+            model_dd = gr.Dropdown(
+                label="Model",
+                choices=model_choices,
+                value=default_model,
+                scale=2,
+            )
+            endpoint_info = gr.Markdown(
+                elem_classes="model-banner",
+                value=f"`{endpoints[default_model].api_base}`  →  `{endpoints[default_model].model_path}`",
+            )
+
+        with gr.Row():
+            prompt_box = gr.Textbox(
+                label="Text Prompt",
+                placeholder="Ask the model anything. Combine with image / audio / video below.",
+                lines=3,
+                value=DEFAULT_PROMPTS[0],
+                scale=3,
+            )
+            with gr.Column(scale=1):
+                tts_checkbox = gr.Checkbox(label="Generate speech output (TTS)", value=True)
+                max_tokens_slider = gr.Slider(label="Max tokens", minimum=32, maximum=4096, value=1024, step=32)
+                temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=1.5, value=0.7, step=0.05)
+
+        with gr.Row(elem_classes="media-row"):
+            image_input = gr.Image(label="Image (optional)", type="pil", sources=["upload"])
+            audio_input = gr.Audio(
+                label="Audio (optional)",
+                type="numpy",
+                sources=["upload", "microphone"],
+            )
+            video_input = gr.Video(label="Video (optional)", sources=["upload"])
+
+        with gr.Row():
+            generate_btn = gr.Button("Generate", variant="primary", elem_id="generate-btn")
+            clear_btn = gr.Button("Clear")
+
+        with gr.Row():
+            text_output = gr.Textbox(label="Text Output", lines=10, scale=2)
+            audio_output = gr.Audio(label="Audio Output", interactive=False, scale=1)
+
+        gr.Examples(
+            examples=[
+                ["你好，请用一句话介绍北京。"],
+                ["Describe what you see in detail."],
+                ["请先识别这段语音的内容，再用中文回复说话人。"],
+                ["What is happening in this video?"],
+            ],
+            inputs=[prompt_box],
+            label="Quick prompts",
+        )
+
+        def _on_model_change(name: str) -> str:
+            ep = endpoints[name]
+            return f"`{ep.api_base}`  →  `{ep.model_path}`"
+
+        model_dd.change(_on_model_change, inputs=[model_dd], outputs=[endpoint_info])
+
+        def _run(
+            model_name: str,
+            prompt: str,
+            image_pil: Image.Image | None,
+            audio_in: Any,
+            video_path: str | None,
+            enable_tts: bool,
+            max_tokens: int,
+            temperature: float,
+        ):
+            ep = endpoints[model_name]
+            audio_tuple = process_audio_input(audio_in)
+            yield from run_inference(
+                ep,
+                prompt,
+                audio_tuple,
+                image_pil,
+                video_path,
+                enable_tts,
+                max_tokens,
+                temperature,
+            )
+
+        generate_btn.click(
+            _run,
+            inputs=[
+                model_dd,
+                prompt_box,
+                image_input,
+                audio_input,
+                video_input,
+                tts_checkbox,
+                max_tokens_slider,
+                temperature_slider,
+            ],
+            outputs=[text_output, audio_output],
+        )
+
+        def _clear():
+            return "", None, None, None, "", None
+
+        clear_btn.click(
+            _clear,
+            outputs=[
+                prompt_box,
+                image_input,
+                audio_input,
+                video_input,
+                text_output,
+                audio_output,
+            ],
+        )
+
+        demo.queue()
+    return demo
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--minicpmo45-api-base", default=os.environ.get("MINICPMO45_API_BASE", "http://localhost:8099/v1"))
+    p.add_argument(
+        "--minicpmo45-model",
+        default=os.environ.get("MINICPMO45_MODEL", "openbmb/MiniCPM-o-4_5"),
+    )
+    p.add_argument("--host", default="0.0.0.0")
+    p.add_argument("--port", type=int, default=7862)
+    p.add_argument("--share", action="store_true")
+    p.add_argument(
+        "--ssl-certfile",
+        default=os.environ.get("GRADIO_SSL_CERTFILE", ""),
+        help="Path to TLS certificate (PEM). Browsers require HTTPS to access the microphone.",
+    )
+    p.add_argument(
+        "--ssl-keyfile",
+        default=os.environ.get("GRADIO_SSL_KEYFILE", ""),
+        help="Path to TLS private key (PEM).",
+    )
+    p.add_argument(
+        "--ssl-verify",
+        action="store_true",
+        default=os.environ.get("GRADIO_SSL_VERIFY", "0") == "1",
+        help="Require browser to verify the certificate (off by default; self-signed certs should stay off).",
+    )
+    return p.parse_args()
+
+
+def _ping(api_base: str, timeout: float = 3.0) -> bool:
+    import urllib.request
+
+    try:
+        with urllib.request.urlopen(api_base.rstrip("/").replace("/v1", "/health"), timeout=timeout):
+            return True
+    except Exception:
+        return False
+
+
+def main() -> None:
+    args = parse_args()
+    endpoints: dict[str, ModelEndpoint] = {}
+
+    if args.minicpmo45_api_base and args.minicpmo45_model:
+        ok = _ping(args.minicpmo45_api_base)
+        status = "reachable" if ok else "not reachable (will error on generate)"
+        print(f"[4.5] {args.minicpmo45_api_base}  ({status})")
+        endpoints[MINICPMO45] = ModelEndpoint(MINICPMO45, args.minicpmo45_api_base, args.minicpmo45_model)
+
+    if not endpoints:
+        raise SystemExit("No endpoints configured. Pass --minicpmo45-api-base/--minicpmo45-model.")
+
+    default_model = next(iter(endpoints.keys()))
+    demo = build_interface(endpoints, default_model)
+
+    launch_kwargs: dict = {
+        "server_name": args.host,
+        "server_port": args.port,
+        "share": args.share,
+    }
+    if args.ssl_certfile and args.ssl_keyfile:
+        launch_kwargs.update(
+            ssl_certfile=args.ssl_certfile,
+            ssl_keyfile=args.ssl_keyfile,
+            ssl_verify=args.ssl_verify,
+        )
+        print(f"[tls] cert={args.ssl_certfile} key={args.ssl_keyfile} verify={args.ssl_verify}")
+    demo.launch(**launch_kwargs)
+
+
+if __name__ == "__main__":
+    main()

examples/online_serving/minicpmo/run_gradio_demo.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+# Launch the MiniCPM-o 4.5 gradio demo.
+#
+# Prereq:
+#   Start a vllm-omni OpenAI server for MiniCPM-o 4.5 on :8099 (see the
+#   8x4090 stage config under vllm_omni/model_executor/stage_configs).
+set -e
+
+HERE="$(cd "$(dirname "$0")" && pwd)"
+
+: "${MINICPMO45_API_BASE:=http://localhost:8099/v1}"
+: "${MINICPMO45_MODEL:=openbmb/MiniCPM-o-4_5}"
+: "${GRADIO_HOST:=0.0.0.0}"
+: "${GRADIO_PORT:=7862}"
+# HTTPS (browsers require a secure context for microphone access).
+# Set GRADIO_SSL_CERTFILE / GRADIO_SSL_KEYFILE to enable TLS.
+: "${GRADIO_SSL_CERTFILE:=}"
+: "${GRADIO_SSL_KEYFILE:=}"
+
+export MINICPMO45_API_BASE MINICPMO45_MODEL
+
+SSL_ARGS=()
+if [ -n "$GRADIO_SSL_CERTFILE" ] && [ -n "$GRADIO_SSL_KEYFILE" ] \
+   && [ -f "$GRADIO_SSL_CERTFILE" ] && [ -f "$GRADIO_SSL_KEYFILE" ]; then
+  SSL_ARGS=(--ssl-certfile "$GRADIO_SSL_CERTFILE" --ssl-keyfile "$GRADIO_SSL_KEYFILE")
+  echo "HTTPS enabled: cert=$GRADIO_SSL_CERTFILE key=$GRADIO_SSL_KEYFILE"
+else
+  echo "HTTPS disabled (cert/key not found). Microphone won't work on remote browsers."
+fi
+
+exec python "$HERE/gradio_demo.py" \
+    --minicpmo45-api-base "$MINICPMO45_API_BASE" \
+    --minicpmo45-model "$MINICPMO45_MODEL" \
+    --host "$GRADIO_HOST" \
+    --port "$GRADIO_PORT" \
+    "${SSL_ARGS[@]}"