Claude/create static website 01 gmff c kq dn z3 acw5 ck9jhxw

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.github/workflows/deploy-pages.yml

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+name: Deploy to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'www/**'
+      - '.github/workflows/deploy-pages.yml'
+  pull_request:
+    branches:
+      - main
+    paths:
+      - 'www/**'
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  # Lint and validate
+  validate:
+    name: Validate HTML/CSS/JS
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Install validation tools
+        run: |
+          npm install -g htmlhint stylelint eslint
+
+      - name: Validate HTML
+        run: |
+          htmlhint www/*.html || true
+
+      - name: Validate CSS
+        run: |
+          npx stylelint "www/css/**/*.css" --allow-empty-input || true
+
+      - name: Validate JavaScript
+        run: |
+          npx eslint "www/js/**/*.js" --no-eslintrc --env browser,es6 || true
+
+      - name: Check for broken links
+        run: |
+          echo "Link checking can be added here with tools like linkinator"
+
+  # Build job (if needed for preprocessing)
+  build:
+    name: Build Website
+    runs-on: ubuntu-latest
+    needs: validate
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+
+      - name: Build site
+        run: |
+          echo "Building static site..."
+          # Add any build steps here if needed (e.g., minification, optimization)
+          # For now, we're using the files as-is
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./www
+
+  # Deployment job
+  deploy:
+    name: Deploy to GitHub Pages
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    if: github.ref == 'refs/heads/main' && github.event_name != 'pull_request'
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4
+
+      - name: Summary
+        run: |
+          echo "🚀 Website deployed successfully!" >> $GITHUB_STEP_SUMMARY
+          echo "URL: ${{ steps.deployment.outputs.page_url }}" >> $GITHUB_STEP_SUMMARY
+
+  # Lighthouse CI for performance testing
+  lighthouse:
+    name: Lighthouse CI
+    runs-on: ubuntu-latest
+    needs: deploy
+    if: github.ref == 'refs/heads/main' && github.event_name != 'pull_request'
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Run Lighthouse CI
+        uses: treosh/lighthouse-ci-action@v10
+        with:
+          urls: |
+            ${{ needs.deploy.outputs.page_url }}
+          uploadArtifacts: true
+          temporaryPublicStorage: true

.gitignore

@@ -4,4 +4,21 @@ venv_linux
 __pycache__
 chroma_db
 .gradio
-.langgraph_api
+.langgraph_api
+.vscode
+=======
+__pycache__
+prep
+.env
+.env.local
+chroma_db
+test.py
+data
+creds
+credentials
+credentials.json
+token.json
+node_modules
+venv
+venv2
+*.db

1-first-agent/.env.example

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+# Rename this file to .env once you have filled in the below environment variables!
+
+# Get your Open AI API Key by following these instructions -
+https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
+OPENAI_API_KEY=
+
+# See all Open AI models you can use here -
+# https://platform.openai.com/docs/models
+# A good default to go with here is gpt-4o
+OPENAI_MODEL=
+
+# Get your personal Asana access token through the developer console in Asana. 
+# Feel free to follow these instructions -
+# https://developers.asana.com/docs/personal-access-token
+ASANA_ACCESS_TOKEN=
+
+# The Asana project ID is in the URL when you visit a project in the Asana UI.
+# If your URL is https://app.asana.com/0/123456789/1212121212, then your
+# Asana project ID is 123456789
+ASANA_PROJECT_ID=

1-first-agent/agents.py

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+import asana
+from asana.rest import ApiException
+from openai import OpenAI
+from dotenv import load_dotenv
+from datetime import datetime
+import json
+import os
+
+load_dotenv()
+
+client = OpenAI()
+model = os.getenv('OPENAI_MODEL', 'gpt-4o')
+
+configuration = asana.Configuration()
+configuration.access_token = os.getenv('ASANA_ACCESS_TOKEN', '')
+api_client = asana.ApiClient(configuration)
+
+tasks_api_instance = asana.TasksApi(api_client)
+
+def create_asana_task(task_name, due_on="today"):
+    """
+    Creates a task in Asana given the name of the task and when it is due
+
+    Example call:
+
+    create_asana_task("Test Task", "2024-06-24")
+    Args:
+        task_name (str): The name of the task in Asana
+        due_on (str): The date the task is due in the format YYYY-MM-DD. If not given, the current day is used
+    Returns:
+        str: The API response of adding the task to Asana or an error message if the API call threw an error
+    """
+    if due_on == "today":
+        due_on = str(datetime.now().date())
+
+    task_body = {
+        "data": {
+            "name": task_name,
+            "due_on": due_on,
+            "projects": [os.getenv("ASANA_PROJECT_ID", "")]
+        }
+    }
+
+    try:
+        api_response = tasks_api_instance.create_task(task_body, {})
+        return json.dumps(api_response, indent=2)
+    except ApiException as e:
+        return f"Exception when calling TasksApi->create_task: {e}"
+
+def get_tools():
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "create_asana_task",
+                "description": "Creates a task in Asana given the name of the task and when it is due",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "task_name": {
+                            "type": "string",
+                            "description": "The name of the task in Asana"
+                        },
+                        "due_on": {
+                            "type": "string",
+                            "description": "The date the task is due in the format YYYY-MM-DD. If not given, the current day is used"
+                        },
+                    },
+                    "required": ["task_name"]
+                },
+            },
+        }
+    ]
+
+    return tools     
+
+def prompt_ai(messages):
+    # First, prompt the AI with the latest user message
+    completion = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        tools=get_tools()
+    )
+
+    response_message = completion.choices[0].message
+    tool_calls = response_message.tool_calls
+
+    # Second, see if the AI decided it needs to invoke a tool
+    if tool_calls:
+        # If the AI decided to invoke a tool, invoke it
+        available_functions = {
+            "create_asana_task": create_asana_task
+        }
+
+        # Add the tool request to the list of messages so the AI knows later it invoked the tool
+        messages.append(response_message)
+
+        # Next, for each tool the AI wanted to call, call it and add the tool result to the list of messages
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+            function_to_call = available_functions[function_name]
+            function_args = json.loads(tool_call.function.arguments)
+            function_response = function_to_call(**function_args)
+
+            messages.append({
+                "tool_call_id": tool_call.id,
+                "role": "tool",
+                "name": function_name,
+                "content": function_response
+            })
+
+        # Call the AI again so it can produce a response with the result of calling the tool(s)
+        second_response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+        )
+
+        return second_response.choices[0].message.content
+
+    return response_message.content
+
+def main():
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are a personal assistant who helps manage tasks in Asana. The current date is: {datetime.now().date()}"
+        }
+    ]
+
+    while True:
+        user_input = input("Chat with AI (q to quit): ").strip()
+
+        if user_input == 'q':
+            break  
+
+        messages.append({"role": "user", "content": user_input})
+        ai_response = prompt_ai(messages)
+
+        print(ai_response)
+        messages.append({"role": "assistant", "content": ai_response})
+
+
+if __name__ == "__main__":
+    main()

1-first-agent/requirements.txt

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+asana==5.0.0
+openai==1.10.0
+python-dotenv==0.13.0

10-deploy-ai-agent-langserve/.env.example

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+# Rename this file to .env once you have filled in the below environment variables!
+
+# No environment variables for Google Drive access!
+# See the below instructions for Google Drive authentication for Python:
+# https://developers.google.com/drive/api/quickstart/python
+
+# See all Open AI models you can use here -
+# https://platform.openai.com/docs/models
+# And all Anthropic models you can use here -
+# https://docs.anthropic.com/en/docs/about-claude/models
+# A good default to go with here is gpt-4o-mini, claude-3-5-sonnet-20240620, or llama3-groq-70b-8192-tool-use-preview
+LLM_MODEL=gpt-4o-mini
+
+# The URL for the LangServe API (not including the specific endpoint but including the port)
+AGENT_ENDPOINT_URL=http://localhost:8000
+
+# Get your GROQ API Key here -
+# https://console.groq.com/keys
+# You only need this environment variable set if you set LLM_MODEL to a Groq model
+GROQ_API_KEY=
+
+# Get your Open AI API Key by following these instructions -
+# https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
+# You only need this environment variable set if you set LLM_MODEL to a GPT model
+OPENAI_API_KEY=
+
+# Get your Anthropic API Key in your account settings -
+# https://console.anthropic.com/settings/keys
+# You only need this environment variable set if you set LLM_MODEL to a Claude model
+ANTHROPIC_API_KEY=
+
+# Get your personal Asana access token through the developer console in Asana. 
+# Feel free to follow these instructions -
+# https://developers.asana.com/docs/personal-access-token
+ASANA_ACCESS_TOKEN=
+
+# The Asana workspace ID is in the URL when you visit your Asana Admin Console (when logged in).
+# Go to the URL "https://app.asana.com/admin" and then your workspace ID
+# will appear in the URL as a slew of digits once the site loads.
+# If your URL is https://app.asana.com/admin/987654321/insights, then your
+# Asana workspace ID is 987654321
+ASANA_WORKPLACE_ID=