Update app.py

app.py

@@ -1,16 +1,4 @@
-import os
-import json
-import torch
-import torch.nn.functional as F
-import timm
-import numpy as np
-import gradio as gr
-import requests
-from io import BytesIO
-from PIL import Image
-from huggingface_hub import hf_hub_download
-
-# ============== 1. 配置参数 ==============
+# ============== 配置参数 ==============
 class Config:
     model_repo = "telecomadm1145/cmodel_v2_test"
     weights_file = "pytorch_model.bin"       
@@ -18,190 +6,90 @@ class Config:
     rev = "6350d5e35f883ca058bbc84a82853407874b68da"
     model_name = "convnext_base.dinov3_lvd1689m"
     image_size = 384
-    
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    # 你的“男娘概念”特征向量 (确保同目录下有这个文件)
     npy_file = "target_vector.npy"
-
 cfg = Config()
 
-# ============== 2. 核心逻辑 ==============
-def download_file(repo_id, filename, rev):
-    print(f"📥 Downloading {filename} from {repo_id}...")
-    try:
-        return hf_hub_download(repo_id=repo_id, filename=filename, revision=rev)
-    except Exception as e:
-        print(f"⚠️ Failed to download {filename}: {e}")
-        return None
-
-# --- 获取 QQ 头像的功能 ---
-def fetch_qq_avatar(qq_num):
-    if not qq_num or not qq_num.isdigit():
-        return None, "❌ 请输入纯数字的有效QQ号！"
-    
-    # 使用 QQ 头像官方接口 (s=640 获取高清图, q1/q2 均可)
-    url = f"http://q1.qlogo.cn/g?b=qq&nk={qq_num}&s=640"
-    try:
-        response = requests.get(url, timeout=5)
-        response.raise_for_status()
-        img = Image.open(BytesIO(response.content)).convert("RGB")
-        return img, "✅ 获取 QQ 头像成功！点击下方按钮开始检测吧~"
-    except Exception as e:
-        return None, f"❌ 获取失败，可能是网络波动或QQ号不存在: {e}"
-
+# ============== 相似度映射 ==============
 def map_similarity(sim: float) -> float:
-    """将余弦相似度 [sim_min, sim_max] 线性映射到 [0, 1]，并裁剪。"""
-    mapped = (sim - 0.8) / (0.9 - 0.8)
+    """将余弦相似度线性映射到 [0, 1]，原始区间 [0.8, 0.9]"""
+    mapped = (sim - 0.75) / (0.85 - 0.75)
     return max(0.0, min(1.0, mapped))
 
-# --- 向量匹配核心 ---
-class SingleVectorMatcher:
-    def __init__(self):
-        print(f"🔄 Initializing on device: {cfg.device}")
-        
-        # 1. 初始化模型架构
-        name_map_path = download_file(cfg.model_repo, cfg.name_mapping_file, "main")
-        with open(name_map_path, 'r', encoding='utf-8') as f:
-            full_map = json.load(f)
-            num_classes = len(full_map.get('id_to_model_name', {})) + len(full_map.get('id_to_base_model', {}))
-
-        self.model = timm.create_model(cfg.model_name, pretrained=False, num_classes=num_classes)
-        
-        # 2. 加载权重
-        weights_path = download_file(cfg.model_repo, cfg.weights_file, cfg.rev)
-        state_dict = torch.load(weights_path, map_location='cpu')
-        self.model.load_state_dict(state_dict)
-        self.model.to(cfg.device)
-        self.model.eval()
-        
-        # 3. 图像预处理
-        self.transform = timm.data.create_transform(
-            input_size=(3, cfg.image_size, cfg.image_size),
-            is_training=False,
-            mean=timm.data.IMAGENET_DEFAULT_MEAN,
-            std=timm.data.IMAGENET_DEFAULT_STD
-        )
-        
-        # 4. 加载单一向量 NPY
-        self.target_tensor = None
-        self._load_single_npy()
-
-    def _load_single_npy(self):
-        if not os.path.exists(cfg.npy_file):
-            print(f"❌ Error: '{cfg.npy_file}' not found.")
-            return
-
-        print(f"🎯 Loading target vector from {cfg.npy_file}...")
-        try:
-            vector_array = np.load(cfg.npy_file)
-            tensor = torch.tensor(vector_array, dtype=torch.float32)
-            if tensor.dim() == 1:
-                tensor = tensor.unsqueeze(0)
-            self.target_tensor = tensor.to(cfg.device)
-            print(f"✅ Target vector loaded. Shape: {self.target_tensor.shape}")
-        except Exception as e:
-            print(f"❌ Failed to load {cfg.npy_file}: {e}")
-
+# ============== 推理输出 ==============
     @torch.no_grad()
     def match(self, image: Image.Image):
         if image is None:
-            return "❌ 请先上传图片或获取 QQ 头像！"
+            return "请先上传图片或输入 QQ 号获取头像。"
         if self.target_tensor is None:
-            return f"❌ 引擎故障：找不到灵魂向量 `{cfg.npy_file}`！"
+            return f"初始化失败：未找到目标向量文件 `{cfg.npy_file}`。"
             
         if image.mode != 'RGB':
             image = image.convert('RGB')
             
-        # 提取图像特征
         img_tensor = self.transform(image).unsqueeze(0).to(cfg.device)
         features = self.model.forward_features(img_tensor)
-        embedding = self.model.forward_head(features, pre_logits=True) 
+        embedding = self.model.forward_head(features, pre_logits=True)
         
-        # 计算余弦相似度
-        raw = F.cosine_similarity(embedding, self.target_tensor).item()
-        similarity = map_similarity(raw)
+        raw_sim = F.cosine_similarity(embedding, self.target_tensor).item()
+        mapped_sim = map_similarity(raw_sim)
         
-        # ================= Meme 文案逻辑 =================
-        if similarity > 0.85:
-            status = "🚨 **最高警报！纯度极高的小男娘！**<br>这什么神仙画风，快让他/她穿上小裙子！"
-            color = "#ff4d4f" # 红色
-        elif similarity > 0.65:
-            status = "👀 **疑似男娘...**<br>成分复杂，眼神逐渐变得不清白，建议严查！"
-            color = "#faad14" # 橙黄
-        elif similarity > 0.45:
-            status = "🤔 **薛定谔的男娘**<br>处于男娘与普通路人的量子叠加态，有点东西但不多。"
-            color = "#1890ff" # 蓝色
+        # 风格判断
+        if mapped_sim > 0.85:
+            label = "高度相似"
+            desc = "图像风格与 Nano Banana 高度吻合，具有强烈的标志性特征。"
+            color = "#4096ff"
+        elif mapped_sim > 0.65:
+            label = "较为相似"
+            desc = "图像风格与 Nano Banana 有一定相似度，部分特征较为接近。"
+            color = "#36cfc9"
+        elif mapped_sim > 0.45:
+            label = "轻微相似"
+            desc = "图像与 Nano Banana 风格存在少量共同特征，整体差异明显。"
+            color = "#9254de"
         else:
-            status = "🗿 **纯爷们 / 铁直女无误**<br>完全没有任何男娘气息，钢铁直，散了吧。"
-            color = "#52c41a" # 绿色
+            label = "风格不符"
+            desc = "图像风格与 Nano Banana 差异显著，几乎不具备相关特征。"
+            color = "#8c8c8c"
             
         md_out = f"""
-        <div style="text-align: center; padding: 30px; border-radius: 15px; background-color: #f8f9fa; border: 2px solid {color}; box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
-            <h1 style="color: {color}; font-size: 4em; margin: 10px 0;">{similarity:.2%}(raw:{raw})</h1>
+        <div style="padding: 24px; border-radius: 10px; border: 1px solid #e8e8e8; background: #fafafa;">
+            <p style="margin: 0 0 6px; font-size: 13px; color: #8c8c8c;">风格相似度评估</p>
+            <h2 style="margin: 0 0 4px; color: {color}; font-size: 2.8em; font-weight: 700;">{mapped_sim:.2%}</h2>
+            <span style="display: inline-block; padding: 2px 10px; border-radius: 4px; background: {color}20; color: {color}; font-size: 13px; font-weight: 600;">{label}</span>
+            <p style="margin: 14px 0 16px; color: #444; font-size: 14px;">{desc}</p>
+            <hr style="border: none; border-top: 1px solid #eee; margin: 0 0 14px;">
+            <div style="font-size: 12px; color: #999; font-family: monospace;">
+                原始余弦相似度（Raw Cosine Similarity）：<b style="color:#555">{raw_sim:.6f}</b><br>
+                映射函数：<code>(x − 0.80) / (0.90 − 0.80)，裁剪至 [0, 1]</code>
+            </div>
         </div>
         """
         return md_out
 
-# ============== 3. 构建 Gradio UI ==============
-try:
-    matcher = SingleVectorMatcher()
-    is_ready = matcher.target_tensor is not None
-except Exception as e:
-    print(f"Initialization Failed: {e}")
-    matcher, is_ready = None, False
-
-def run_inference(image):
-    if not is_ready:
-         return "❌ 系统未就绪，请确保 `target_vector.npy` 已经上传至根目录。"
-    return matcher.match(image)
 
-# 自定义 CSS 让界面更二次元/Meme一点
-css = """
-.gradio-container { font-family: 'Comic Sans MS', 'Microsoft YaHei', sans-serif !important; }
-"""
-
-with gr.Blocks(title="小男娘浓度检测器", theme=gr.themes.Soft(primary_hue="pink"), css=css) as demo:
+with gr.Blocks(title="Nano Banana 风格检测") as demo:
     gr.Markdown(
         """
-        <div style="text-align: center;">
-            <h1>小男娘浓度检测器</h1>
-            <p>基于先进的深度学习卷积神经网络（确信），精准检测你的头像成分！<br>
-            <i>只需输入 QQ 号，或者直接上传图片即可判定！</i></p>
-        </div>
+        ## Nano Banana 风格相似度检测
+        上传图片，检测其视觉风格与 Nano Banana 的相似程度。支持直接上传图片或通过 QQ 号拉取头像。
         """
     )
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Group():
-                gr.Markdown("### 方式一：一键查成分")
-                with gr.Row():
-                    qq_input = gr.Textbox(label="", placeholder="输入TA的QQ号...", scale=3, show_label=False)
-                    qq_btn = gr.Button("🔍 获取头像", variant="secondary", scale=1)
-            
-            gr.Markdown("### 方式二：自己传黑照")
-            input_img = gr.Image(type="pil", label="被检测目标")
-            
-            # 主按钮
-            btn = gr.Button("开始成分鉴定", variant="primary", size="lg")
-            
+    with gr.Row(equal_height=True):
         with gr.Column(scale=1):
-            out_md = gr.Markdown("### 鉴定报告将在此生成...")
+            input_img = gr.Image(type="pil", label="输入图片")
+            btn = gr.Button("开始检测", variant="primary")
 
-    # 事件绑定
-    qq_btn.click(fetch_qq_avatar, inputs=[qq_input], outputs=[input_img, out_md])
+        with gr.Column(scale=1):
+            out_md = gr.Markdown("检测结果将在此显示。")
     btn.click(run_inference, inputs=[input_img], outputs=[out_md])
-    
-    # 免责声明 (Disclaimer)
+
     gr.Markdown(
         """
         ---
-        ### ⚠️ 免责声明 (Disclaimer)
-        1. **仅供娱乐**：本页面仅供群友整活、Meme（梗）交流与娱乐使用。
-        2. **AI 的数学游戏**：本程序的“浓度”仅代表图像高维矩阵特征与特定预设模型向量的**余弦相似度（Cosine Similarity）**。
-        3. **无关真实身份**：检测结果**绝对不代表**任何真实人物的生理性别、身份认同、性取向或道德评价。
-        4. **请勿滥用**：请大家图一乐就好，**严禁**将本测试结果用于网暴、人身攻击、造谣或任何严肃场合。最终解释权归作者所有。
+        **说明：** 相似度基于图像高维嵌入与预设参考向量的余弦相似度计算，结果仅供参考，不代表对风格的完整评价。  
+        原始输出区间约为 [.75, .85+]，经线性映射后展示为百分比。
         """
     )