wehub-resource-sync
130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
"""
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End-to-end smoke test for image upload + multimodal inference (mock LLM).
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Registers an openai-agents agent, creates a runner-bound session, uploads
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an image via the session-scoped files API, posts a user message with an
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``input_image`` content block referencing the file, and verifies the
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agent produces non-empty text. The mock LLM returns a canned response
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describing the image colors.
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Usage::
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pytest tests/e2e/test_image_upload_e2e.py -v
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"""
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from __future__ import annotations
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import uuid
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from pathlib import Path
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import httpx
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from tests.e2e.conftest import (
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configure_mock_llm,
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create_runner_bound_session,
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poll_session_until_terminal,
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register_inline_agent,
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reset_mock_llm,
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send_user_message_to_session,
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)
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from tests.e2e.helpers import final_assistant_text
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_REPO_ROOT = Path(__file__).resolve().parents[2]
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# Checked-in test image: 100x100 red square with a blue center.
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_TEST_IMAGE_PATH = _REPO_ROOT / "tests" / "resources" / "test_image.png"
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def test_image_upload_reaches_llm(
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http_client: httpx.Client,
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live_runner_id: str,
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mock_llm_server_url: str,
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) -> None:
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"""
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Upload an image, send it to an agent, verify the agent produces
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non-empty text describing the image.
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Full AP-side e2e:
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1. Register an openai-agents agent pointing at the mock LLM.
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2. Create a runner-bound session and upload a test PNG via the
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session-scoped files API.
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3. Post a user message (text + ``input_image``) asking the model
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to identify the dominant color; poll the snapshot until terminal.
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4. Assert the mock response text appears in the output (proving
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the image upload pipeline didn't drop content before reaching
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the executor).
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"""
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model = f"mock-image-{uuid.uuid4().hex[:6]}"
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reset_mock_llm(mock_llm_server_url)
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agent_name = register_inline_agent(
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http_client,
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name=f"image-e2e-{uuid.uuid4().hex[:6]}",
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harness="openai-agents",
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model=model,
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profile="",
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prompt=(
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"You are a vision assistant. When the user sends an "
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"image, describe what you see. Be specific about "
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"colors, shapes, and content."
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),
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mock_llm_base_url=f"{mock_llm_server_url}/v1",
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)
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# The mock returns a canned description mentioning the test image colors.
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configure_mock_llm(
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mock_llm_server_url,
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[{"text": "The image shows a red square with a blue center."}],
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key=model,
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)
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session_id = create_runner_bound_session(
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http_client,
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agent_name=agent_name,
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runner_id=live_runner_id,
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)
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assert _TEST_IMAGE_PATH.exists(), (
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f"Test image missing at {_TEST_IMAGE_PATH}. Run the generate script or restore from git."
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)
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image_bytes = _TEST_IMAGE_PATH.read_bytes()
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file_resp = http_client.post(
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f"/v1/sessions/{session_id}/resources/files",
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files={"file": ("test_image.png", image_bytes, "image/png")},
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)
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file_resp.raise_for_status()
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file_id = file_resp.json()["id"]
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response_id = send_user_message_to_session(
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http_client,
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session_id=session_id,
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content=[
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{
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"type": "input_text",
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"text": (
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"What is the dominant color of this image? Reply with just the color name."
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),
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},
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{"type": "input_image", "file_id": file_id},
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],
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)
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body = poll_session_until_terminal(
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http_client,
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session_id=session_id,
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response_id=response_id,
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timeout=120,
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)
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assert body["status"] == "completed", f"response failed: {body.get('error', 'unknown')}"
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text = final_assistant_text(body).lower().strip()
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assert text, "no assistant output text in response"
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# The mock returns "red" and "blue" — verify it came through.
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assert "red" in text or "blue" in text, (
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f"LLM did not identify any color in the image — "
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f"multimodal content likely dropped before reaching "
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f"the model. Full response:\n{text}"
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)
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