如何使用 Cloud Storage、Firestore 和 Cloud Run 上传和传送图片
关于此 Codelab
subject上次更新时间：4月 4, 2025
1. 简介
概览
在此 Codelab 中，您将学习如何使用 Cloud Storage、Firestore 和 Cloud Run 上传和传送图片。您还将了解如何使用 Google 的客户端库进行身份验证，以便调用 Gemini。
学习内容
- 如何将 FastAPI 应用部署到 Cloud Run
- 如何使用 Google 的客户端库进行身份验证
- 如何使用 Cloud Run 服务将文件上传到 Cloud Storage
- 如何读取和写入 Firestore 中的数据
- 如何在 Cloud Run 服务中从 Cloud Storage 检索和显示图片
2. 设置和要求
设置将在此 Codelab 中全程使用的环境变量。
PROJECT_ID=dogfood-gcf-saraford
REGION=us-central1
GCS_BUCKET_NAME=dogfood-gcf-saraford-codelab-wietse-2
SERVICE_NAME=fastapi-storage-firestore
SERVICE_ACCOUNT=fastapi-storage-firestore-sa
SERVICE_ACCOUNT_ADDRESS=$SERVICE_ACCOUNT@$PROJECT_ID.iam.gserviceaccount.com
启用 API
gcloud services enable run.googleapis.com \
storage.googleapis.com \
firestore.googleapis.com \
cloudbuild.googleapis.com \
artifactregistry.googleapis.com
创建一个 Cloud Storage 存储分区来存储图片
gsutil mb -p dogfood-gcf-saraford -l us-central1 gs://$GCS_BUCKET_NAME
允许公开访问您可以在网站上上传和显示图片的存储分区：
gsutil iam ch allUsers:objectViewer gs://$GCS_BUCKET_NAME
运行以下命令来创建服务账号：
gcloud iam service-accounts create $SERVICE_ACCOUNT \
--display-name="SA for CR $SERVICE_ACCOUNT"
并向该 SA 授予对 Firestore 和 GCS 存储分区的访问权限
gcloud projects add-iam-policy-binding $PROJECT_ID \
--member="serviceAccount:$SERVICE_ACCOUNT_ADDRESS" \
--role="roles/datastore.user"
gsutil iam ch serviceAccount:$SERVICE_ACCOUNT_ADDRESS:roles/storage.objectAdmin gs://$GCS_BUCKET_NAME
4. 创建应用
为您的代码创建一个目录。
mkdir codelab-cr-fastapi-firestore-gcs
cd codelab-cr-fastapi-firestore-gcs
首先，您需要创建一个模板目录，以便创建 html 模板。
mkdir templates
cd templates
创建一个名为 index.html 且包含以下内容的新文件：
<!DOCTYPE html>
<html>
<head>
<title>Cloud Run Image Upload Demo</title>
<style>
body { font-family: sans-serif; padding: 20px; }
.upload-form { margin-bottom: 20px; padding: 15px; border: 1px solid #ccc; border-radius: 5px; background-color: #f9f9f9; }
.image-list { margin-top: 30px; }
.image-item { border-bottom: 1px solid #eee; padding: 10px 0; }
.image-item img { max-width: 100px; max-height: 100px; vertical-align: middle; margin-right: 10px;}
.error { color: red; font-weight: bold; margin-top: 10px;}
</style>
</head>
<body>
<h1>Upload an Image</h1>
<p>Files will be uploaded to GCS bucket: <strong>{{ bucket_name }}</strong> and metadata stored in Firestore.</p>
<div class="upload-form">
<form action="/upload" method="post" enctype="multipart/form-data">
<input type="file" name="file" accept="image/*" required>
<button type="submit">Upload Image</button>
</form>
{% if error_message %}
<p class="error">{{ error_message }}</p>
{% endif %}
</div>
<div class="image-list">
<h2>Recently Uploaded Images:</h2>
{% if images %}
{% for image in images %}
<div class="image-item">
<a href="{{ image.gcs_url }}" target="_blank">
<img src="{{ image.gcs_url }}" alt="{{ image.filename }}" title="Click to view full size">
</a>
<span>{{ image.filename }}</span>
<small>(Uploaded: {{ image.uploaded_at.strftime('%Y-%m-%d %H:%M:%S') if image.uploaded_at else 'N/A' }})</small><br/>
<small><a href="{{ image.gcs_url }}" target="_blank">{{ image.gcs_url }}</a></small>
</div>
{% endfor %}
{% else %}
<p>No images uploaded yet or unable to retrieve list.</p>
{% endif %}
</div>
</body>
</html>
现在，在根目录中创建 Python 代码和其他文件
cd ..
创建一个包含以下内容的 .gcloudignore 文件：
__pycache__
创建一个包含以下内容的 main.py 文件：
import os
import datetime
from fastapi import FastAPI, File, UploadFile, Request, Form
from fastapi.responses import HTMLResponse, RedirectResponse
from fastapi.templating import Jinja2Templates
from google.cloud import storage, firestore
# --- Configuration ---
# Get bucket name and firestore collection from Cloud Run env vars
GCS_BUCKET_NAME = os.environ.get("GCS_BUCKET_NAME", "YOUR_BUCKET_NAME_DEFAULT")
FIRESTORE_COLLECTION = os.environ.get("FIRESTORE_COLLECTION", "YOUR_FIRESTORE_DEFAULT")
# --- Initialize Google Client Libraries ---
# These client libraries will use the Application Default Credentials
# for your service account within the Cloud Run environment
storage_client = storage.Client()
firestore_client = firestore.Client()
# --- FastAPI App ---
app = FastAPI()
templates = Jinja2Templates(directory="templates")
# --- Routes ---
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
"""Serves the main upload form."""
# Query Firestore for existing images to display
images = []
try:
docs = firestore_client.collection(FIRESTORE_COLLECTION).order_by(
"uploaded_at", direction=firestore.Query.DESCENDING
).limit(10).stream() # Get latest 10 images
for doc in docs:
images.append(doc.to_dict())
except Exception as e:
print(f"Warning: Could not fetch images from Firestore: {e}")
# Continue without displaying images if Firestore query fails
return templates.TemplateResponse("index.html", {
"request": request,
"bucket_name": GCS_BUCKET_NAME,
"images": images # Pass images to the template
})
@app.post("/upload")
async def handle_upload(request: Request, file: UploadFile = File(...)):
"""Handles file upload, saves to GCS, and records in Firestore."""
if not file:
return {"message": "No upload file sent"}
elif not GCS_BUCKET_NAME or GCS_BUCKET_NAME == "YOUR_BUCKET_NAME_DEFAULT":
return {"message": "GCS Bucket Name not configured."}, 500 # Internal Server Error
try:
# 1. Upload to GCS
# note: to keep the demo code short, there are no file verifications
# for an actual real-world production app, you will want to add checks
gcs_url = upload_to_gcs(file, GCS_BUCKET_NAME)
# 2. Save metadata to Firestore
save_metadata_to_firestore(file.filename, gcs_url, FIRESTORE_COLLECTION)
# Redirect back to the main page after successful upload
return RedirectResponse(url="/", status_code=303) # Redirect using See Other
except Exception as e:
print(f"Upload failed: {e}")
return templates.TemplateResponse("index.html", {
"request": request,
"bucket_name": GCS_BUCKET_NAME,
"error_message": f"Upload failed: {e}",
"images": [] # Pass empty list on error or re-query
}, status_code=500)
# --- Helper Functions ---
def upload_to_gcs(uploadedFile: UploadFile, bucket_name: str) -> str:
"""Uploads a file to Google Cloud Storage and returns the public URL."""
try:
bucket = storage_client.bucket(bucket_name)
# Create a unique blob name (e.g., timestamp + original filename)
timestamp = datetime.datetime.now(datetime.timezone.utc).strftime("%Y%m%d%H%M%S")
blob_name = f"{timestamp}_{uploadedFile.filename}"
blob = bucket.blob(blob_name)
# Upload the file
# Reset file pointer just in case
uploadedFile.file.seek(0)
blob.upload_from_file(uploadedFile.file, content_type=uploadedFile.content_type)
print(f"File {uploadedFile.filename} uploaded to gs://{bucket_name}/{blob_name}")
return blob.public_url # Return the public URL
except Exception as e:
print(f"Error uploading to GCS: {e}")
raise # Re-raise the exception for FastAPI to handle
def save_metadata_to_firestore(filename: str, gcs_url: str, collection_name: str):
"""Saves image metadata to Firestore."""
try:
doc_ref = firestore_client.collection(collection_name).document()
doc_ref.set({
'filename': filename,
'gcs_url': gcs_url,
'uploaded_at': firestore.SERVER_TIMESTAMP # Use server timestamp
})
print(f"Metadata saved to Firestore collection {collection_name}")
except Exception as e:
print(f"Error saving metadata to Firestore: {e}")
# Consider raising the exception or handling it appropriately
raise # Re-raise the exception
创建一个包含以下内容的 Dockerfile：
# Build stage
FROM python:3.12-slim AS builder
WORKDIR /app
# Install poetry
RUN pip install poetry
RUN poetry self add poetry-plugin-export
# Copy poetry files
COPY pyproject.toml poetry.lock* ./
# Copy application code
COPY . .
# Export dependencies to requirements.txt
RUN poetry export -f requirements.txt --output requirements.txt
# Final stage
FROM python:3.12-slim
WORKDIR /app
# Copy files from builder
COPY --from=builder /app/ .
# Install dependencies
RUN pip install --no-cache-dir -r requirements.txt
# Compile bytecode to improve startup latency
# -q: Quiet mode
# -b: Write legacy bytecode files (.pyc) alongside source
# -f: Force rebuild even if timestamps are up-to-date
RUN python -m compileall -q -b -f .
# Expose port
EXPOSE 8080
# Run the application
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
并创建了以下 pyproject.toml
[tool.poetry]
name = "cloud-run-fastapi-demo"
version = "0.1.0"
description = "Demo FastAPI app for Cloud Run showing GCS upload and Firestore integration."
authors = ["Your Name <you@example.com>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.12"
fastapi = "^0.110.0"
uvicorn = {extras = ["standard"], version = "^0.29.0"} # Includes python-multipart
google-cloud-storage = "^2.16.0"
google-cloud-firestore = "^2.16.0"
jinja2 = "^3.1.3"
python-multipart = "^0.0.20"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
5. 部署到 Cloud Run
以下是用于部署到 Cloud Run 的命令。系统会压缩您的代码并将其发送到 Cloud Build，Cloud Build 会使用 Dockerfile 创建映像。
由于这是基于源代码的 Cloud Run 部署，因此在该服务的 Cloud 控制台中，您会看到包含代码的“Source”（源代码）标签页。
gcloud run deploy $SERVICE_NAME \
--source . \
--allow-unauthenticated \
--service-account=$SERVICE_ACCOUNT_ADDRESS \
--set-env-vars=GCS_BUCKET_NAME=$GCS_BUCKET_NAME \
--set-env-vars=FIRESTORE_COLLECTION=$FIRESTORE_COLLECTION
6. 测试您的服务
在网络浏览器中打开服务网址，然后上传图片。您会在列表中看到该应用。
7. 更改公开 Cloud Storage 存储分区的权限
如前所述，此 Codelab 使用的是公共 GCS 存储分区。建议您删除该存储分区，或者运行以下命令移除对该存储分区的 allUsers 访问权限：
gsutil iam ch -d allUsers:objectViewer gs://$GCS_BUCKET_NAME
您可以通过运行以下命令来确认已移除 allUsers 访问权限：
gsutil iam get gs://$GCS_BUCKET_NAME
8. 恭喜
恭喜您完成此 Codelab！
所学内容
- 如何将 FastAPI 应用部署到 Cloud Run
- 如何使用 Google 的客户端库进行身份验证
- 如何使用 Cloud Run 服务将文件上传到 Cloud Storage
- 如何读取和写入 Firestore 中的数据
- 如何在 Cloud Run 服务中从 Cloud Storage 检索和显示图片
9. 清理
如需删除 Cloud Run 服务，请前往 Cloud Run Cloud 控制台 (https://console.cloud.google.com/run) 并删除该服务。
如需删除 Cloud Storage 存储分区，您可以运行以下命令：
echo "Deleting objects in gs://$GCS_BUCKET_NAME..."
gsutil rm -r gs://$GCS_BUCKET_NAME/*
echo "Deleting bucket gs://$GCS_BUCKET_NAME..."
gsutil rb gs://$GCS_BUCKET_NAME
如果您选择删除整个项目，可以前往 https://console.cloud.google.com/cloud-resource-manager，选择您在第 2 步中创建的项目，然后选择“删除”。如果您删除该项目，则需要在 Cloud SDK 中更改项目。您可以通过运行 gcloud projects list 来查看所有可用项目的列表。