| import os |
| import base64 |
| from io import BytesIO |
| from PIL import Image |
| import gradio as gr |
| from openai import OpenAI |
|
|
| def get_client(): |
| token = os.environ.get("HF_TOKEN") |
| if not token: |
| raise ValueError("找不到 HF_TOKEN 環境變數，請確認是否已在 Space Settings 中設定 Secrets。") |
| return OpenAI( |
| base_url="https://router.huggingface.co/v1", |
| api_key=token, |
| ) |
|
|
| def compress_image_to_base64(image_path, max_size=1024, quality=85): |
| """將圖片縮放並壓縮轉為 Base64，避免 413 Payload Too Large 錯誤""" |
| img = Image.open(image_path) |
| |
| |
| if img.mode in ("RGBA", "P"): |
| img = img.convert("RGB") |
| |
| |
| img.thumbnail((max_size, max_size)) |
| |
| buffered = BytesIO() |
| img.save(buffered, format="JPEG", quality=quality) |
| encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8") |
| return f"data:image/jpeg;base64,{encoded_string}" |
|
|
| def analyze_image(image, prompt): |
| if image is None: |
| return "請先上傳一張圖片！" |
| |
| if not prompt or prompt.strip() == "": |
| prompt = "Describe this image in one sentence." |
|
|
| try: |
| |
| base64_image = compress_image_to_base64(image) |
| |
| client = get_client() |
| completion = client.chat.completions.create( |
| model="google/gemma-4-31B-it:novita", |
| messages=[ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "text", "text": prompt}, |
| { |
| "type": "image_url", |
| "image_url": {"url": base64_image} |
| } |
| ] |
| } |
| ], |
| max_tokens=300 |
| ) |
| return completion.choices[0].message.content |
| except Exception as e: |
| return f"發生錯誤：{str(e)}" |
|
|
| demo = gr.Interface( |
| fn=analyze_image, |
| inputs=[ |
| gr.Image(type="filepath", label="上傳圖片"), |
| gr.Textbox(label="輸入提示詞 (Prompt)", value="Describe this image in one sentence in chinese") |
| ], |
| outputs=gr.Textbox(label="模型回應"), |
| title="Gemma 多模態影像分析助手", |
| description="透過 Hugging Face Router 呼叫 gemma-4-31B-it 模型進行影像辨識與分析。" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
