添加 Web 演示截图与 Gradio 部署教程到 README

不小心上传空白csv，现已恢复

.gitignore

@@ -5,10 +5,13 @@
 !/Merge_classes.py
 !/Model.py
 !/README.md
+!/requirements.txt
 !/THIRD_PARTY_LICENSES.md
 !/Train.py
+!/web/
+!/web/app.py
+!/web/README.md
 !/Baseline.py
-!/AGENTS.md
 !/Finetune.py
 !/Curve.py
 !/Evaluate.py
@@ -21,6 +24,8 @@
 !/baseline/roc_comparison.png
 !/baseline/pr_comparison.png
 !/baseline/accuracy_bar.png
+!/web/
+!/web/online.jpg
 !/training_log.csv
 !/confusion_matrix.png
 !/roc_curve.png

AGENTS.md

@@ -1,84 +0,0 @@
-# AGENTS.md
-
-## Project
-
-CNN-based garbage classification (4 classes: 厨余垃圾/可回收物/其他垃圾/有害垃圾). ResNet-34 architecture, ~21M params, 256×256 RGB input, ~900 lines across 11 Python files. No package structure.
-
-## Pipeline (order matters)
-
-```bash
-python Merge_classes.py                 # merges 265 → 4 classes, creates ../trash_division_data/ultimate_4_class/
-python Train.py                         # trains the model, saves best_model.pth + training_log.csv
-python Finetune.py                      # optional: freezes early layers, saves finetuned_model.pth + finetune_log.csv
-python Evaluate.py                      # plots confusion matrix / ROC / PR curves from best_model.pth
-python Curve.py                         # plots loss/f1/acc/lr curves from training_log.csv
-python baseline/VGG_KNN.py             # VGG16 feature extraction + KNN baseline
-python baseline/compare_models.py      # compares multiple models (ROC + accuracy bar chart)
-```
-
-Also usable standalone: `python Model.py` prints `torchsummary` parameter summary.
-
-## Dependencies
-
-No `requirements.txt` — install manually: `torch`, `torchvision`, `tqdm`, `matplotlib`, `pandas`, `Pillow`, `torchsummary`. `Evaluate.py` and `baseline/*.py` additionally need `scikit-learn`.
-
-## Data setup
-
-Data expected **outside repo** at `../trash_division_data/` (sibling dir). `Merge_classes.py` reads `val/classname.txt` there; `Train.py` and `Finetune.py` expect `ultimate_4_class/{train,val}/` with class-numbered subdirs (`1/` to `4/`). All paths relative to repo root.
-
-## .gitignore — whitelist pattern
-
-`.gitignore` uses `*` (ignore everything) then un-ignores specific files with `!` patterns. **Any new file you add to the repo must be explicitly whitelisted** or it will be invisible to git. The current whitelist includes: `Dataloader.py`, `LICENSE`, `Merge_classes.py`, `Model.py`, `README.md`, `THIRD_PARTY_LICENSES.md`, `Train.py`, `.gitattributes`, `.gitignore`, plus `Finetune.py`, `Curve.py`, `Evaluate.py`, `AGENTS.md`, 4× output PNG, `training_log.csv`, and `baseline/`.
-
-`best_model.pth` and `finetuned_model.pth` are **untracked** (~125 MB each) — back them up manually if needed. `Finetune.py`, `Curve.py`, `Evaluate.py`, `AGENTS.md`, `training_log*.csv`, and `finetune_log.csv` are also untracked (not in whitelist).
-
-## Gotchas
-
-- **Windows: set `num_workers=0`** in `create_dataloaders()` call sites (`Train.py:191`, `Finetune.py:196`, `Dataloader.py:229`)
-- Device selection priority: `cuda > xpu > cpu` (`xpu` = Intel GPU)
-- Training auto-resumes from `best_model.pth` if present in repo root; fine-tuning auto-loads it too
-- `Dataloader.py` uses `RobustImageFolder` — scans all images, skips corrupted ones (tqdm progress), slow on first load
-- Image normalization: hardcoded ImageNet stats (`mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`)
-- `create_dataloaders()` has a `val_split` parameter that's **never used** — the code always expects a pre-split `val/` folder
-
-### Finetune-specific
-
-- **BUG**: `freeze_base_layers()` references `model.stage2` and `model.stage3` but the model uses `layer2`/`layer3`. This crashes at runtime — fix to `model.layer2`/`model.layer3` (or delete the function, since it would freeze `layer2`+`layer3` while docstring says only conv1+stage2).
-- `freeze_base_layers()` actually freezes **conv1, bn1, layer2, AND layer3** (despite docstring saying only conv1 + stage2). Only layer1, layer4, and fc are trainable.
-- Class weights use `power=1.5` (vs `power=1.0` in Train) — amplifies minority-class weighting
-- Defaults: `lr=0.0001`, `epochs=30` (vs `lr=0.001`, `epochs=20` in Train)
-- Writes `finetune_log.csv` (Train writes `training_log.csv`)
-- Loads `best_model.pth` then saves `finetuned_model.pth`
-
-### Curve.py
-
-- Hardcoded to read `training_log.csv` only — won't work for `finetune_log.csv`
-- Requires `pandas`, saves `training_curves.png`
-
-### Evaluate.py
-
-- Hardcoded constants at top of `__main__` block: `MODEL_PATH`, `DATA_ROOT`, `BATCH_SIZE`, `NUM_WORKERS`
-- Loads model from `best_model.pth` by default; handles both bare state_dict and `model_state_dict`/`model` key wrappers
-- Saves `confusion_matrix.png`, `roc_curve.png`, `pr_curve.png`
-- Requires `scikit-learn`
-
-### baseline/ (VGG_KNN.py + compare_models.py)
-
-- `baseline/VGG_KNN.py` can run standalone (`python baseline/VGG_KNN.py`) or be imported from `compare_models.py`
-- Uses `sys.path.insert` at top so it can import root-level modules (`Model`, `Dataloader`) from subdirectory
-- `compare_models.py` has a `MODELS` registry list — add new models by writing a `get_xxx_preds(train_loader, val_loader, device)` function and adding one line to the list; no plot code changes needed
-- VGG16 feature dimension: 25088 (512 channels × 7×7 avgpool)
-- KNN uses `predict_proba` (neighbor voting proportions) for ROC curves — coarse-grained but valid AUC
-- Output: `baseline/roc_comparison.png`, `baseline/accuracy_bar.png`, `baseline/vgg_knn_confusion_matrix.png`
-- Compare scripts output images to `baseline/` dir (not repo root)
-
-## Model architecture reference
-
-`Model.py` attribute names (for freezing / layer access):
-- `conv1`, `bn1`, `relu`, `maxpool`
-- `layer1`, `layer2`, `layer3`, `layer4`
-- `avgpool`, `dropout` (nn.Dropout), `fc` (nn.Linear(512, 4))
-
-## Testing
-
-No test suite.

README.md

@@ -6,11 +6,14 @@
 
 基于 ResNet-34 架构的 CNN 模型（约 21M 参数），将生活垃圾分为厨余垃圾、可回收物、其他垃圾、有害垃圾四个类别，输入为 256×256 RGB 图像。
 
+![demo](web/online.jpg)
+
 ---
 
 ## 目录
 
 - [项目特点](#项目特点)
+- [Web 演示](#web-演示)
 - [模型架构](#模型架构)
 - [数据集](#数据集)
 - [环境要求](#环境要求)
@@ -34,6 +37,36 @@
 - **断点续训**：自动检测 `best_model.pth` 并加载继续训练
 - **多设备支持**：自动选择 CUDA > Intel XPU > CPU
 
+## Web 演示
+
+本项目提供基于 Gradio 的 Web 界面，上传图片即可实时预测垃圾类别。
+
+![demo](web/online.jpg)
+
+### 启动
+
+1. 确保 `best_model.pth` 位于项目根目录（如没有，先运行 `python Train.py`）
+2. 安装 Gradio 依赖：
+   ```bash
+   pip install "gradio>=4.0,<5.0" "pydantic>=2.5,<2.10"
+   ```
+3. 启动 Web 服务：
+   ```bash
+   python web/app.py
+   ```
+4. 浏览器打开 `http://127.0.0.1:7860`
+
+### 配置
+
+可在 `web/app.py` 底部 `demo.launch()` 中调整：
+
+| 参数 | 默认值 | 说明 |
+|---|---|---|
+| `server_name` | `127.0.0.1` | 局域网访问改为 `0.0.0.0` |
+| `server_port` | `7860` | 端口冲突时可换 |
+| `share` | `False` | 改为 `True` 可生成临时公网链接 |
+| `inbrowser` | `True` | 启动后自动打开浏览器 |
+
 ## 模型架构
 
 模型基于标准 ResNet-34 架构，使用 BasicBlock 构建。
@@ -75,18 +108,14 @@
 
 ## 环境要求
 
-本项目无 `requirements.txt`，需手动安装以下依赖：
+```bash
+pip install -r requirements.txt
+```
 
-- Python 3.8+
-- PyTorch（推荐 1.10+）
-- torchvision
-- tqdm
-- matplotlib
-- pandas
-- Pillow
-- torchsummary
-- scikit-learn（仅 `Evaluate.py` 需要）
-- scikit-image（仅 `baseline/HOG_Baseline.py` 需要）
+> **注意**：`requirements.txt` 不锁定 PyTorch 的 CUDA / XPU 版本，请根据硬件自行安装对应版本，例如：
+> - NVIDIA GPU：`pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121`
+> - Intel GPU (XPU)：`pip install torch torchvision --index-url https://download.pytorch.org/whl/xpu` 安装
+> - CPU：`pip install torch torchvision` 即可
 
 ## 快速开始
 
@@ -136,16 +165,15 @@
 | `baseline/ResNet34_Pretrained_10pct.py` | ResNet-34 ImageNet 预训练 + 10% 数据微调 |
 | `baseline/HOG_Baseline.py` | HOG + 颜色直方图 + LogisticRegression（纯传统 CV） |
 | `baseline/compare_models.py` | 多模型对比（ROC / PR 曲线 + 准确率柱状图） |
+| `web/app.py` | Gradio Web 前端，上传图片实时分类 |
 | `training_log.csv` | 训练日志，记录每轮 epoch 的 loss、f1、acc、lr |
 | `best_model.pth` | 训练好的最佳模型权重（约 125 MB，不纳入版本控制） |
-| `AGENTS.md` | AI 助手指南（开发辅助） |
 | `THIRD_PARTY_LICENSES.md` | 第三方数据集许可证声明 |
 
 ## 目录结构
 
 ```
 trash-division/
-├── AGENTS.md               # AI 助手指南
 ├── baseline/                # 基线模型目录
 │   ├── VGG_KNN.py           # VGG16 + KNN 分类脚本
 │   ├── ResNet34_Pretrained_10pct.py  # ResNet-34 ImageNet 预训练 + 10% 微调
@@ -154,6 +182,9 @@ trash-division/
 │   ├── roc_comparison.png   # 多模型 ROC 对比（compare_models.py 输出）
 │   ├── pr_comparison.png    # 多模型 PR 对比（compare_models.py 输出）
 │   └── accuracy_bar.png     # 多模型准确率对比（compare_models.py 输出）
+├── web/                      # Web 前端目录
+│   ├── app.py                # Gradio 应用入口
+│   └── online.jpg            # Web 演示截图
 ├── best_model.pth           # 最佳模型权重（不纳入版本控制）
 ├── Curve.py                 # 训练曲线绘制脚本
 ├── Dataloader.py            # 数据加载模块

requirements.txt

@@ -0,0 +1,12 @@
+torch>=1.10
+torchvision>=0.11
+gradio>=4.0,<5.0
+pydantic>=2.5,<2.10
+tqdm
+matplotlib
+pandas
+Pillow
+scikit-learn
+scikit-image
+numpy
+torchsummary

training_log.csv

@@ -1 +1,81 @@
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web/README.md

@@ -0,0 +1,62 @@
+# Trash Division Web 前端
+
+基于 Gradio 的垃圾分类识别 Web 应用，上传图片即可预测垃圾类别。
+
+## 依赖
+
+除项目根目录 `requirements.txt` 外，Web 前端额外依赖：
+
+| 包 | 版本 | 说明 |
+|---|---|---|
+| `gradio` | `>=4.0,<5.0` | Web UI 框架 |
+| `pydantic` | `>=2.5,<2.10` | gradio 4.x 兼容性约束（新版会报 `"const" in schema` 错误） |
+
+> 安装：`pip install gradio>=4.0,<5.0 pydantic>=2.5,<2.10`
+
+## 启动前准备
+
+1. **确保 `best_model.pth` 存在**  
+   在项目根目录（`trash-division/`）下放置训练好的模型权重。如没有，先运行：
+   ```bash
+   cd .. && python Train.py
+   ```
+
+2. **安装依赖**（如还未安装）：
+   ```bash
+   pip install -r ../requirements.txt
+   ```
+
+## 启动
+
+在 `web/` 目录下运行：
+
+```bash
+python app.py
+```
+
+或者在项目根目录运行：
+
+```bash
+python web/app.py
+```
+
+启动后浏览器会自动打开 `http://127.0.0.1:7860`。
+
+## 配置说明
+
+可在 `app.py` 底部 `demo.launch()` 中调整以下参数：
+
+| 参数 | 默认值 | 说明 |
+|---|---|---|
+| `server_name` | `127.0.0.1` | 本机访问。如需局域网内其他设备访问，改为 `0.0.0.0` |
+| `server_port` | `7860` | 端口号，冲突时可换 |
+| `share` | `False` | 改为 `True` 可生成临时公网链接分享给同学 |
+| `inbrowser` | `True` | 启动后自动打开浏览器 |
+
+## 兼容性
+
+| 项 | 说明 |
+|---|---|
+| Python | `>=3.9,<3.10`（Gradio 5.x 需 Python 3.10+） |
+| PyTorch | `>=1.10` |
+| 设备 | 自动选择 CUDA > Intel XPU > Apple MPS > CPU |

web/app.py

@@ -0,0 +1,104 @@
+import sys
+import os
+# 确保可以从 web/ 目录或项目根目录运行，都能找到 Model.py 和 best_model.pth
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+from Model import Net  # 根据上传的 Model.py，模型类名为 Net
+
+# 项目根目录（web/ 的上一级）
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# 1. 基础配置与类别映射
+# 根据 Merge_classes.py，1=厨余垃圾, 2=可回收物, 3=其他垃圾, 4=有害垃圾
+class_names = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+
+# 设备自动选择逻辑，保持与 Train.py 和 Evaluate.py 一致
+device = torch.device('cuda' if torch.cuda.is_available() else 'xpu' if torch.xpu.is_available() else 'mps' if torch.mps.is_available() else 'cpu')
+print(f"当前使用的推理设备: {device}")
+
+# 2. 初始化模型并加载最佳权重
+model = Net(num_classes=4)
+try:
+    # 采用与 Evaluate.py 一致的健壮加载方式
+    model_path = os.path.join(PROJECT_ROOT, 'best_model.pth')
+    state_dict = torch.load(model_path, map_location=device)
+    if 'model_state_dict' in state_dict:
+        state_dict = state_dict['model_state_dict']
+    elif 'model' in state_dict:
+        state_dict = state_dict['model']
+        
+    model.load_state_dict(state_dict)
+    model = model.to(device).eval()
+    print("✅ 成功加载 best_model.pth 权重")
+except Exception as e:
+    print(f"⚠️ 模型加载失败，请确保目录下存在 best_model.pth。错误信息: {e}")
+
+# 3. 定义数据预处理流程 (必须与 Evaluate.py 中的 val_transform 保持完全一致)
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+# 4. 核心推理函数
+def predict(image):
+    if image is None:
+        return None
+    
+    # Gradio 传入的 pil 图像，确保转为 RGB 格式
+    image = image.convert('RGB')
+    
+    # 预处理并增加 batch 维度
+    input_tensor = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        # 使用 Softmax 将 logits 转换为 0~1 的概率分布
+        probabilities = torch.softmax(outputs, dim=1)[0]
+    
+    # 组装为 Gradio Label 组件需要的字典格式 { "类别名": 概率值 }
+    result_dict = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
+    return result_dict
+
+# 5. 构建与美化 Gradio 界面
+with gr.Blocks(theme=gr.themes.Soft(), title="Trash Division 垃圾分类识别") as demo:
+    gr.Markdown(
+        """
+        <div style="text-align: center; max-width: 800px; margin: 0 auto;">
+            <h1>🗑️ Trash Division - 智能垃圾分类系统</h1>
+            <p>基于 <b>ResNet-34</b> 架构，支持精准识别：<b>厨余垃圾、可回收物、其他垃圾、有害垃圾</b>。</p>
+            <p><i>同济大学 Python 人工智能程序设计课程小组作业</i></p>
+        </div>
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # type="pil" 让 Gradio 直接传 PIL Image 对象给预测函数，配合 torchvision 最方便
+            image_input = gr.Image(type="pil", label="上传垃圾图片 (支持拍照)")
+            with gr.Row():
+                clear_btn = gr.Button("清空", variant="secondary")
+                submit_btn = gr.Button("开始识别", variant="primary")
+        
+        with gr.Column(scale=1):
+            label_output = gr.Label(num_top_classes=4, label="预测结果与置信度")
+            
+    # 绑定点击事件
+    submit_btn.click(fn=predict, inputs=image_input, outputs=label_output)
+    clear_btn.click(lambda: (None, None), inputs=None, outputs=[image_input, label_output])
+
+if __name__ == "__main__":
+    # 启动 Web 界面
+    demo.launch(
+        server_name="127.0.0.1",
+        server_port=7860,
+        share=False,  # 如果你想生成临时公网链接分享给同学测试，改为 True
+        inbrowser=True  # 运行后自动在默认浏览器中打开
+    )