upgrade gradio

.gitignore

@@ -0,0 +1,125 @@
+.gradio/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db

app.py

@@ -2,6 +2,9 @@ from pathlib import Path
 import torch
 import gradio as gr
 from torch import nn
+import numpy as np
+
+print(gr.__version__)
 
 LABELS = Path("class_names.txt").read_text().splitlines()
 
@@ -20,27 +23,58 @@ model = nn.Sequential(
     nn.ReLU(),
     nn.Linear(256, len(LABELS)),
 )
+
 state_dict = torch.load("pytorch_model.bin", map_location="cpu")
 model.load_state_dict(state_dict, strict=False)
 model.eval()
 
-
 def predict(im):
+    if im is None:
+        return {}
+    
+    # 处理输入图像
+    # 如果是字典格式（新版Gradio Sketchpad的输出），提取图像
+    if isinstance(im, dict):
+        im = im['image'] if 'image' in im else im.get('composite', None)
+    
+    # 转换为numpy数组并确保是灰度图
+    if isinstance(im, np.ndarray):
+        if len(im.shape) == 3:
+            # 如果是RGB图像，转换为灰度图
+            im = np.mean(im, axis=2)
+    else:
+        return {}
+    
+    # 确保图像尺寸正确（28x28）
+    if im.shape != (28, 28):
+        from PIL import Image
+        im_pil = Image.fromarray(im.astype('uint8'))
+        im_pil = im_pil.resize((28, 28))
+        im = np.array(im_pil)
+    
+    # 转换为tensor并进行预测
     x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0
+    
     with torch.no_grad():
         out = model(x)
+    
     probabilities = torch.nn.functional.softmax(out[0], dim=0)
     values, indices = torch.topk(probabilities, 5)
+    
     return {LABELS[i]: v.item() for i, v in zip(indices, values)}
 
+# 创建Gradio界面
 interface = gr.Interface(
-    predict,
-    inputs="sketchpad",
-    outputs="label",
-    theme="huggingface",
+    fn=predict,
+    inputs=gr.Sketchpad(
+        image_mode="L",  # 灰度模式
+        canvas_size=(280, 280),  # 画布大小
+        brush=gr.Brush(default_size=10)  # 画笔设置
+    ),
+    outputs=gr.Label(num_top_classes=5),  # 显示前5个预测结果
     title="Sketch Recognition",
     description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!",
     article="<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
-    live=True,
 )
+
 interface.launch(share=True)

requirements.txt

@@ -1,2 +1,2 @@
 torch
-gradio==3.50.0
+gradio