Update app.py

app.py

@@ -1,154 +1,134 @@
+import spaces  # 必须在最顶部导入
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+import os
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+# 获取 Hugging Face 访问令牌
+hf_token = os.getenv("HF_API_TOKEN")
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+# 定义基础模型名称
+base_model_name = "larry1129/WooWoof_AI_Vision_merged_16bit"
 
-        result = gr.Image(label="Result", show_label=False)
+# 定义 adapter 模型名称
+adapter_model_name = "larry1129/WooWoof_AI_Vision_merged_16bit"
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+# 定义全局变量用于缓存模型和分词器
+model = None
+tokenizer = None
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+# 定义提示生成函数
+def generate_prompt(instruction, input_text=""):
+    if input_text:
+        prompt = f"""### Instruction:
+{instruction}
+### Input:
+{input_text}
+### Response:
+"""
+    else:
+        prompt = f"""### Instruction:
+{instruction}
+### Response:
+"""
+    return prompt
+
+# 定义生成响应的函数，并使用 @spaces.GPU 装饰
+@spaces.GPU(duration=40)  # 建议将 duration 增加到 120
+def generate_response(instruction, input_text):
+    global model, tokenizer
+
+    if model is None:
+        print("开始加载模型...")
+        # 检查 bitsandbytes 是否已安装
+        import importlib.util
+        if importlib.util.find_spec("bitsandbytes") is None:
+            import subprocess
+            subprocess.call(["pip", "install", "--upgrade", "bitsandbytes"])
+
+        try:
+            # 在函数内部导入需要 GPU 的库
+            import torch
+            from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+            from peft import PeftModel
+
+            # 创建量化配置
+            bnb_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_compute_dtype=torch.float16
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+            # 加载分词器
+            tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_auth_token=hf_token)
+            print("分词器加载成功。")
+
+            # 加载基础模型
+            base_model = AutoModelForCausalLM.from_pretrained(
+                base_model_name,
+                quantization_config=bnb_config,
+                device_map="auto",
+                use_auth_token=hf_token,
+                trust_remote_code=True
+            )
+            print("基础模型加载成功。")
+
+            # 加载适配器模型
+            model = PeftModel.from_pretrained(
+                base_model,
+                 adapter_model_name,
+                torch_dtype=torch.float16,
+                use_auth_token=hf_token
+            )
+            print("适配器模型加载成功。")
+
+            # 设置 pad_token
+            tokenizer.pad_token = tokenizer.eos_token
+            model.config.pad_token_id = tokenizer.pad_token_id
+
+            # 切换到评估模式
+            model.eval()
+            print("模型已切换到评估模式。")
+        except Exception as e:
+            print("加载模型时出错：", e)
+            raise e
+    else:
+        # 在函数内部导入需要的库
+        import torch
+
+    # 检查 model 和 tokenizer 是否已正确加载
+    if model is None or tokenizer is None:
+        print("模型或分词器未正确加载。")
+        raise ValueError("模型或分词器未正确加载。")
+
+    # 生成提示
+    prompt = generate_prompt(instruction, input_text)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs.get("attention_mask"),
+            max_new_tokens=128,
+            temperature=0.7,
+            top_p=0.95,
+            do_sample=True,
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.split("### Response:")[-1].strip()
+    return response
+
+# 创建 Gradio 接口
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Instruction", label="Instruction"),
+    ],
+    outputs="text",
+    title="WooWoof AI",
+    description="Based on LLAMA 3.1 for pet related",
+    allow_flagging="never"
+)
+
+# 启动 Gradio 接口
+iface.launch(share=True)