Added code from Kristopher Overholt

.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

Procfile

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+web: python app.py

README.md

@@ -9,5 +9,59 @@ app_file: app.py
 pinned: false
 license: mit
 ---
+# palm-vertex-ai-chatbot
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+An example chatbot app with Gradio that uses the PaLM API from Vertex AI in
+Google Cloud
+
+![Chatbot app powered by the PaLM API in Google Cloud](images/chatbot.png)
+
+## Usage
+
+1. Clone this repository.
+
+2. Install the required dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. In the GCP Console, navigate to `Artificial Intelligence > Vertex AI > Dashboard`, and enable the
+   [Vertex AI API](https://console.cloud.google.com/apis/enableflow?apiid=aiplatform.googleapis.com).
+
+4. Optionally, if you want to run the Streamlit app on Railway, navigate to `APIs & Services > Enabled APIs & services`, and select Vertex AI API from the list. From the `Credentials tab`, click `Create Credentials > Service account`
+(https://console.cloud.google.com/iam-admin/serviceaccounts/create)
+, and create a service account with the Vertex AI User role. Finally, add and download a new key for this service account i.e. JSON credentials file.
+
+5. Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable, e.g.
+
+   ```bash
+   export GOOGLE_APPLICATION_CREDENTIALS='/tmp/vertex-ai-user.json'
+   ```
+
+4. Run the app:
+
+   ```bash
+   python app.py
+   ```
+
+5. Navigate to `http://127.0.0.1:8080/` in your browser.
+
+6. Start chatting with your PaLM-powered chatbot!
+
+## Deploy to Cloud Run
+
+The above steps allow you to test and use the chatbot app on your local machine.
+You can use a fully managed service such as Cloud Run to publish your app.
+
+1. Run the following command using the
+   [gcloud CLI](https://cloud.google.com/sdk/gcloud):
+
+   ```bash
+   gcloud run deploy chatbot-app --source . --allow-unauthenticated --region us-central1
+   ```
+
+2. After a couple of minutes, your chatbot app will be deployed on Cloud Run,
+   and you can access the app via a URL similar to:
+
+   [`https://chatbot-app-r5gdynozbq-uc.a.run.app/`](https://chatbot-app-r5gdynozbq-uc.a.run.app/)

app.py

@@ -1,7 +1,55 @@
 import gradio as gr
+from vertexai.preview.language_models import ChatModel, InputOutputTextPair, ChatSession
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def create_session():
+    chat_model = ChatModel.from_pretrained("chat-bison@001")
+    parameters = {
+        "temperature": 0.2,
+        "max_output_tokens": 256,
+        "top_k": 40,
+        "top_p": 0.80,
+        "context": "You are an astronomer, knowledgeable about the solar system.",
+        "examples": [
+            InputOutputTextPair(
+                input_text="How many moons does Mars have?",
+                output_text="The planet Mars has two moons, Phobos and Deimos.",
+            ),
+        ],
+    }
+    chat = ChatSession(model=chat_model, **parameters)
+    return chat
+
+
+def response(chat, message):
+    res = chat.send_message(
+        message=message, max_output_tokens=256, temperature=0.2, top_k=40, top_p=0.8
+    )
+    return res.text
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+    [
+        (
+        "Hello! I'm an astronomer chatbot.\nAsk me anything about the solar system!",
+        None,
+        )
+    ],
+        label="PaLM-based Chatbot powered by Google Cloud",
+    ).style(height=400)
+    msg = gr.Textbox(placeholder="Type your response here", label="Response")
+    clear = gr.Button("Clear")
+    chat_model = create_session()
+
+    def respond(message, chat_history):
+        texts = [message]
+        bot_message = response(chat_model, message)
+        chat_history.append((message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch(server_port=8080)