Update app.py

app.py

@@ -1,6 +1,6 @@
 import torch
 import gradio as gr
-from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM
 from deepseek_vl.models import VLChatProcessor
 from deepseek_vl.utils.io import load_pil_images
 
@@ -11,50 +11,47 @@ model_path = "deepseek-ai/deepseek-vl-7b-chat"
 vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
 tokenizer = vl_chat_processor.tokenizer
 
-# ==== 量化模型設定 (4-bit) ====
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.float16,
-    bnb_4bit_use_double_quant=True
-)
-
+# ==== 載入模型 (CPU/GPU 兼容，不使用量化) ====
 vl_gpt: AutoModelForCausalLM = AutoModelForCausalLM.from_pretrained(
     model_path,
     trust_remote_code=True,
-    device_map="auto",
-    quantization_config=bnb_config
+    device_map="auto",       # 自動分配 GPU / CPU
+    torch_dtype=torch.bfloat16  # 低精度減少 VRAM
 )
 vl_gpt.eval()
 
 # ==== 單張圖片處理 + 減少 max_new_tokens ====
 def generate_answer(image, text):
-    conversation = [
-        {"role": "User", "content": "<image_placeholder>" + text, "images": [image]},
-        {"role": "Assistant", "content": ""}
-    ]
+    try:
+        conversation = [
+            {"role": "User", "content": "<image_placeholder>" + text, "images": [image]},
+            {"role": "Assistant", "content": ""}
+        ]
 
-    pil_images = load_pil_images(conversation)
-    prepare_inputs = vl_chat_processor(
-        conversations=conversation,
-        images=pil_images,
-        force_batchify=True
-    ).to(vl_gpt.device)
+        pil_images = load_pil_images(conversation)
+        prepare_inputs = vl_chat_processor(
+            conversations=conversation,
+            images=pil_images,
+            force_batchify=True
+        ).to(vl_gpt.device)
 
-    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+        inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
 
-    outputs = vl_gpt.language_model.generate(
-        inputs_embeds=inputs_embeds,
-        attention_mask=prepare_inputs.attention_mask,
-        pad_token_id=tokenizer.eos_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-        max_new_tokens=128,  # 降低生成長度
-        do_sample=False,
-        use_cache=True
-    )
+        outputs = vl_gpt.language_model.generate(
+            inputs_embeds=inputs_embeds,
+            attention_mask=prepare_inputs.attention_mask,
+            pad_token_id=tokenizer.eos_token_id,
+            bos_token_id=tokenizer.bos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            max_new_tokens=128,   # 降低生成長度
+            do_sample=False,
+            use_cache=True
+        )
 
-    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
-    return f"{prepare_inputs['sft_format'][0]} {answer}"
+        answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
+        return f"{prepare_inputs['sft_format'][0]} {answer}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # ==== Gradio Web UI ====
 demo = gr.Interface(
@@ -62,7 +59,7 @@ demo = gr.Interface(
     inputs=[gr.Image(type="pil", label="Upload Image"), gr.Textbox(label="Question")],
     outputs="text",
     title="DeepSeek-VL-7B Chat Demo",
-    description="上傳圖片並輸入問題，模型會生成與圖片相關的回答（4-bit 量化，低記憶體模式）"
+    description="上傳圖片並輸入問題，模型會生成與圖片相關的回答（CPU/GPU 兼容，低記憶體模式）"
 )
 
 if __name__ == "__main__":