:sparkles: add a couple apps

.gitignore

@@ -0,0 +1,162 @@
+# 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/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.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
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# 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/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.DS_Store

README.md

@@ -4,6 +4,7 @@ emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
+sdk_version: 2.9b23
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,18 +1,12 @@
 import gradio as gr
 
-
-def main(text):
-    # A gradio function that lowercases text and returns it
-    return text.lower()
-
-
-# A simple gradio app for text
-app = gr.Interface(
-    main,
-    inputs="text",
-    outputs="text",
-    title="gradio-guides",
-    description="A collection of gradio apps from the gradio guides",
-)
-app.launch(debug=True)
-
+import chatbot.app
+import pictionary.app
+
+gr.TabbedInterface(
+    interface_list=[
+        chatbot.app.interface,
+        pictionary.app.interface,
+    ],
+    tab_names=['chatbot', 'pictionary'],
+).launch(debug=True)

chatbot/app.py

@@ -0,0 +1,35 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+    # convert the tokens to text, and then split the responses into the right format
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
+    return response, history
+
+
+import gradio as gr
+
+interface = gr.Interface(
+    fn=predict,
+    theme="default",
+    css=".footer {display:none !important}",
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+)
+
+if __name__ == '__main__':
+    interface.launch()

packages.txt

@@ -0,0 +1,3 @@
+ffmpeg 
+libsm6 
+libxext6

pictionary/app.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import gradio as gr
+import torch
+from huggingface_hub import hf_hub_download
+from torch import nn
+
+LABELS = Path(hf_hub_download('nateraw/quickdraw', 'class_names.txt')).read_text().splitlines()
+
+model = nn.Sequential(
+    nn.Conv2d(1, 32, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(32, 64, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(64, 128, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Flatten(),
+    nn.Linear(1152, 256),
+    nn.ReLU(),
+    nn.Linear(256, len(LABELS)),
+)
+weights_file = hf_hub_download('nateraw/quickdraw', 'pytorch_model.bin')
+state_dict = torch.load(weights_file, map_location='cpu')
+model.load_state_dict(state_dict, strict=False)
+model.eval()
+
+
+def predict(im):
+    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)}
+
+
+interface = gr.Interface(predict, inputs='sketchpad', outputs='label', live=True)
+
+if __name__ == '__main__':
+    interface.launch(debug=True)

requirements.txt

@@ -1,2 +1,8 @@
 gradio
-
+torch
+transformers
+opencv-python
+onnxruntime==1.6.0
+numpy
+matplotlib
+onnx