Create app.py

app.py

@@ -0,0 +1,156 @@
+import streamlit as st
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+import time
+
+# --- 1. 页面基础配置 ---
+st.set_page_config(
+    page_title="Llama 3.2 AI Assistant",
+    page_icon="🤖",
+    layout="wide",  # 使用宽屏模式
+    initial_sidebar_state="expanded"
+)
+
+# --- 2. 自定义 CSS (美化界面) ---
+st.markdown("""
+<style>
+    /* 隐藏 Streamlit 默认的汉堡菜单和页脚 */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    
+    /* 调整主容器的顶部 padding，让内容更紧凑 */
+    .block-container {
+        padding-top: 2rem;
+        padding-bottom: 2rem;
+    }
+    
+    /* 美化侧边栏 */
+    section[data-testid="stSidebar"] {
+        background-color: #f7f9fc; /* 浅灰蓝背景 */
+    }
+    
+    /* 自定义标题样式 */
+    .title-text {
+        font-family: 'Helvetica Neue', sans-serif;
+        font-weight: 700;
+        font-size: 2.5rem;
+        color: #1E88E5; /* 科技蓝 */
+        text-align: center;
+        margin-bottom: 20px;
+    }
+    
+    .subtitle-text {
+        font-family: 'Helvetica Neue', sans-serif;
+        font-weight: 400;
+        font-size: 1.1rem;
+        color: #666;
+        text-align: center;
+        margin-bottom: 40px;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# --- 3. 标题区域 ---
+st.markdown('<div class="title-text">🤖 Llama 3.2-3B AI Assistant</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtitle-text">Powered by Marcus719/Llama-3.2-3B-changedata-Lab2-GGUF</div>', unsafe_allow_html=True)
+
+# --- 4. 侧边栏 (控制面板) ---
+with st.sidebar:
+    st.image("https://huggingface.co/front/assets/huggingface_logo-noborder.svg", width=50)
+    st.header("⚙️ 控制面板")
+    
+    # 参数设置
+    temperature = st.slider("Temperature (创造性)", min_value=0.1, max_value=1.5, value=0.7, step=0.1, help="值越高，回答越随机；值越低，回答越严谨。")
+    max_tokens = st.slider("Max Tokens (最大长度)", min_value=64, max_value=2048, value=512, step=64)
+    
+    st.divider()
+    
+    # 系统提示词 (System Prompt)
+    system_prompt = st.text_area(
+        "系统设定 (System Prompt)", 
+        value="You are a helpful and polite AI assistant.",
+        height=100
+    )
+    
+    st.divider()
+    
+    # 清除历史按钮
+    if st.button("🗑️ 清除对话历史", use_container_width=True):
+        st.session_state.messages = []
+        st.rerun()
+
+    st.markdown("---")
+    st.markdown("Optimization: **Unsloth Q4_K_M**")
+
+# --- 5. 模型加载逻辑 ---
+REPO_ID = "Marcus719/Llama-3.2-3B-changedata-Lab2-GGUF"
+FILENAME = "unsloth.Q4_K_M.gguf"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=4096,
+        n_threads=2, # HF Spaces free tier limit
+        verbose=False
+    )
+    return llm
+
+try:
+    if "llm" not in st.session_state:
+        with st.spinner("🚀 正在启动 AI 引擎，请稍候..."):
+            st.session_state.llm = load_model()
+except Exception as e:
+    st.error(f"模型加载失败: {e}")
+
+# --- 6. 聊天逻辑 ---
+
+# 初始化历史
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# 显示历史消息
+for message in st.session_state.messages:
+    # 设置不同的头像
+    avatar = "🧑‍💻" if message["role"] == "user" else "🤖"
+    with st.chat_message(message["role"], avatar=avatar):
+        st.markdown(message["content"])
+
+# 处理用户输入
+if prompt := st.chat_input("在此输入您的问题..."):
+    # 1. 显示用户输入
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user", avatar="🧑‍💻"):
+        st.markdown(prompt)
+
+    # 2. 生成 AI 回复
+    with st.chat_message("assistant", avatar="🤖"):
+        message_placeholder = st.empty()
+        full_response = ""
+        
+        # 构建带 System Prompt 的消息列表
+        messages_payload = [{"role": "system", "content": system_prompt}] + [
+            {"role": m["role"], "content": m["content"]}
+            for m in st.session_state.messages
+        ]
+        
+        stream = st.session_state.llm.create_chat_completion(
+            messages=messages_payload,
+            stream=True,
+            max_tokens=max_tokens,
+            temperature=temperature
+        )
+        
+        for chunk in stream:
+            if "content" in chunk["choices"][0]["delta"]:
+                token = chunk["choices"][0]["delta"]["content"]
+                full_response += token
+                # 模拟打字机效果，稍微平滑一点显示
+                message_placeholder.markdown(full_response + "▌")
+        
+        message_placeholder.markdown(full_response)
+    
+    # 3. 保存回复
+    st.session_state.messages.append({"role": "assistant", "content": full_response})