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Update app.py
Browse files
app.py
CHANGED
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@@ -9,7 +9,7 @@ set_seed(42)
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# 从环境变量获取 token
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HF_TOKEN = os.environ.get('HF_TOKEN')
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-
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# 初始化模型管道
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try:
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@@ -20,17 +20,22 @@ try:
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torch_dtype="auto"
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)
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MODEL_LOADED = True
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except Exception as e:
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print(f"❌ 本地模型加载失败: {e}")
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MODEL_LOADED = False
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protein_generator = None
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def generate_with_local_model(instruction, max_length=100):
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"""使用本地加载的模型生成蛋白质序列"""
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if not MODEL_LOADED or protein_generator is None:
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return "❌ 模型未正确加载,请检查控制台日志"
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try:
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result = protein_generator(
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instruction,
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@@ -72,7 +77,6 @@ def generate_with_api(instruction, max_length=100):
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}
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}
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print("🔄 正在调用 API...")
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response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
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if response.status_code == 200:
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@@ -94,34 +98,33 @@ def generate_with_api(instruction, max_length=100):
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def generate_protein(instruction, max_length=100):
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"""主生成函数"""
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time.sleep(0.5)
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try:
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# 显示当前模式
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mode = "本地模型" if MODEL_LOADED else "API调用"
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yield f"🔄 使用 {mode} 处理中..."
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# 优先使用本地模型,失败时使用API
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if MODEL_LOADED:
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result = generate_with_local_model(instruction, max_length)
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else:
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result = generate_with_api(instruction, max_length)
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except Exception as e:
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# 创建界面
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with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
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gr.Markdown("""
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# 🧬 ProtTeX 蛋白质生成器
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**使用自然语言指令生成蛋白质序列**
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*当前模式: {
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""")
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with gr.Row():
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@@ -150,7 +153,6 @@ with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
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output = gr.Textbox(
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label="🧬 生成的蛋白质序列",
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lines=8
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# 移除了 show_copy_button 参数
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)
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# 示例部分
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@@ -163,7 +165,10 @@ with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
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["Design a protein with enzymatic activity for hydrolysis"],
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["Generate a stable protein for high temperature environments"]
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],
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inputs=[instruction]
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)
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# 连接按钮事件
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@@ -177,8 +182,9 @@ with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
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gr.Markdown(f"""
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---
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**系统状态**:
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- 运行模式: {
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- Token
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- 硬件: CPU Basic
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*基于 [mzcwd/ProtTeX](https://huggingface.co/mzcwd/ProtTeX) 模型*
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# 从环境变量获取 token
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HF_TOKEN = os.environ.get('HF_TOKEN')
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token_status = "✅ 已设置" if HF_TOKEN else "❌ 未设置"
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# 初始化模型管道
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try:
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torch_dtype="auto"
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)
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MODEL_LOADED = True
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model_status = "✅ 本地模型加载成功"
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print(model_status)
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except Exception as e:
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print(f"❌ 本地模型加载失败: {e}")
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MODEL_LOADED = False
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protein_generator = None
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model_status = "❌ 本地模型加载失败,使用API模式"
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# 确定运行模式
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if MODEL_LOADED:
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run_mode = "🧠 本地模型"
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else:
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run_mode = "🌐 API调用"
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def generate_with_local_model(instruction, max_length=100):
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"""使用本地加载的模型生成蛋白质序列"""
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try:
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result = protein_generator(
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instruction,
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}
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}
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response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
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if response.status_code == 200:
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def generate_protein(instruction, max_length=100):
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"""主生成函数"""
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if not instruction or instruction.strip() == "":
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return "❌ 请输入有效的蛋白质生成指令"
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# 显示处理状态
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time.sleep(0.5)
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try:
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# 优先使用本地模型,失败时使用API
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if MODEL_LOADED:
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result = generate_with_local_model(instruction, max_length)
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else:
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result = generate_with_api(instruction, max_length)
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return result
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except Exception as e:
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return f"❌ 生成过程中出现未预期错误: {str(e)}"
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# 创建界面
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with gr.Blocks(title="ProtTeX 蛋白质生成器") as demo:
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gr.Markdown(f"""
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# 🧬 ProtTeX 蛋白质生成器
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**使用自然语言指令生成蛋白质序列**
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*当前模式: {run_mode} | Token状态: {token_status}*
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""")
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with gr.Row():
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output = gr.Textbox(
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label="🧬 生成的蛋白质序列",
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lines=8
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)
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# 示例部分
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["Design a protein with enzymatic activity for hydrolysis"],
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["Generate a stable protein for high temperature environments"]
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],
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inputs=[instruction],
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outputs=[output],
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fn=generate_protein,
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cache_examples=False
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)
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# 连接按钮事件
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gr.Markdown(f"""
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---
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**系统状态**:
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- 运行模式: {run_mode}
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- Token状态: {token_status}
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- 模型状态: {model_status}
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- 硬件: CPU Basic
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*基于 [mzcwd/ProtTeX](https://huggingface.co/mzcwd/ProtTeX) 模型*
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