chore: import upstream snapshot with attribution

examples/flows/standard/web-classification-maf/workflow.py

@@ -0,0 +1,190 @@
+import asyncio
+import json
+import os
+from dataclasses import dataclass
+
+import bs4
+import requests
+from dotenv import load_dotenv
+from typing_extensions import Never
+
+from agent_framework import Agent, Executor, WorkflowBuilder, WorkflowContext, handler
+from agent_framework.openai import OpenAIChatClient
+
+load_dotenv()
+
+_SUMMARIZE_SYSTEM_PROMPT = """\
+Please summarize the following text in one paragraph. 100 words.
+Do not add any information that is not in the text."""
+
+_CLASSIFY_SYSTEM_PROMPT = """\
+Your task is to classify a given url into one of the following categories:
+Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
+The classification will be based on the url, the webpage text content summary, or both."""
+
+_EXAMPLES = [
+    {
+        "url": "https://play.google.com/store/apps/details?id=com.spotify.music",
+        "text_content": (
+            "Spotify is a free music and podcast streaming app with millions of songs, albums, and "
+            "original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. "
+            "It has a variety of features such as creating and sharing music playlists, discovering "
+            "new music, and listening to popular and exclusive podcasts. It also has a Premium "
+            "subscription option which allows users to download and listen offline, and access "
+            "ad-free music. It is available on all devices and has a variety of genres and artists "
+            "to choose from."
+        ),
+        "category": "App",
+        "evidence": "Both",
+    },
+    {
+        "url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw",
+        "text_content": (
+            "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL "
+            "games on YouTube. It is available in 2023 and is subject to the terms and privacy policy "
+            "of Google LLC. It is also subject to YouTube's terms of use and any applicable laws."
+        ),
+        "category": "Channel",
+        "evidence": "URL",
+    },
+    {
+        "url": "https://arxiv.org/abs/2303.04671",
+        "text_content": (
+            "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and "
+            "receiving not only languages but also images, providing complex visual questions or "
+            "visual editing instructions, and providing feedback and asking for corrected results. "
+            "It incorporates different Visual Foundation Models and is publicly available. Experiments "
+            "show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with "
+            "the help of Visual Foundation Models."
+        ),
+        "category": "Academic",
+        "evidence": "Text content",
+    },
+    {
+        "url": "https://ab.politiaromana.ro/",
+        "text_content": "There is no content available for this text.",
+        "category": "None",
+        "evidence": "None",
+    },
+]
+
+
+def _fetch_text_content_from_url(url: str) -> str:
+    try:
+        headers = {
+            "User-Agent": (
+                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+                "(KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35"
+            )
+        }
+        response = requests.get(url, headers=headers)
+        if response.status_code == 200:
+            soup = bs4.BeautifulSoup(response.text, "html.parser")
+            return soup.get_text()[:2000]
+        else:
+            return "No available content"
+    except Exception:
+        return "No available content"
+
+
+def _format_examples() -> str:
+    parts = []
+    for ex in _EXAMPLES:
+        parts.append(
+            f'URL: {ex["url"]}\n'
+            f'Text content: {ex["text_content"]}\n'
+            f'OUTPUT:\n'
+            f'{{"category": "{ex["category"]}", "evidence": "{ex["evidence"]}"}}\n'
+        )
+    return "\n".join(parts)
+
+
+class FetchAndSummarizeExecutor(Executor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        client = OpenAIChatClient(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+        )
+        self._agent = Agent(
+            client=client,
+            name="SummarizeAgent",
+            instructions=_SUMMARIZE_SYSTEM_PROMPT,
+        )
+
+    @handler
+    async def process(self, url: str, ctx: WorkflowContext[str]) -> None:
+        text_content = _fetch_text_content_from_url(url)
+        response = await self._agent.run(f"Text: {text_content}\nSummary:")
+        await ctx.send_message(response.text)
+
+
+@dataclass
+class ClassifyInput:
+    url: str
+    summary: str
+
+
+class ClassifyExecutor(Executor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        client = OpenAIChatClient(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+        )
+        self._agent = Agent(
+            client=client,
+            name="ClassifyAgent",
+            instructions=_CLASSIFY_SYSTEM_PROMPT,
+        )
+
+    @handler
+    async def classify(self, summary: str, ctx: WorkflowContext[Never, dict]) -> None:
+        examples_text = _format_examples()
+        prompt = (
+            f'The selection range of the value of "category" must be within '
+            f'"Movie", "App", "Academic", "Channel", "Profile", "PDF" and "None".\n'
+            f'The selection range of the value of "evidence" must be within '
+            f'"Url", "Text content", and "Both".\n'
+            f"Here are a few examples:\n{examples_text}\n"
+            f"For a given URL and text content, classify the url to complete the "
+            f"category and indicate evidence:\n"
+            f"URL: (see text content)\n"
+            f"Text content: {summary}.\nOUTPUT:"
+        )
+        response = await self._agent.run(prompt)
+        try:
+            result = json.loads(response.text)
+        except Exception:
+            result = {"category": "None", "evidence": "None"}
+        await ctx.yield_output(result)
+
+
+def create_workflow():
+    """Create a fresh workflow instance.
+
+    MAF workflows do not support concurrent execution, so each
+    concurrent caller needs its own workflow instance.
+    """
+    _fetch_summarize = FetchAndSummarizeExecutor(id="fetch_and_summarize")
+    _classify = ClassifyExecutor(id="classify")
+    return (
+        WorkflowBuilder(name="WebClassificationWorkflow", start_executor=_fetch_summarize)
+        .add_edge(_fetch_summarize, _classify)
+        .build()
+    )
+
+
+async def main():
+    workflow = create_workflow()
+    url = "https://play.google.com/store/apps/details?id=com.twitter.android"
+    result = await workflow.run(url)
+    output = result.get_outputs()[0]
+    print(f"Category: {output.get('category')}")
+    print(f"Evidence: {output.get('evidence')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())