1. Introduzione
Cosa creerai
Il chiosco AI di Formula E è un'esperienza interattiva basata sulla piattaforma Gemini e sull'API Google GenAI Interactions. Gli utenti si scattano una foto, scelgono uno scenario di Formula E (ad esempio, festeggiare sul podio o partire dalla corsia dei box) e lasciano che Gemini li trasformi in un pilota di Formula E accanto a un'auto da corsa elettrica Gen4.
Focus sull'apprendimento interattivo
In questo codelab, scriverai la logica di AI generativa di base utilizzando l'API Interactions sulla piattaforma Gemini (enterprise=True). Completerai le lacune del codice chiave per:
- Inizializzazione del client: inizializzazione di
genai.Client(enterprise=True)per la piattaforma Gemini. - Gemini 3.8 Flash: genera un prompt dettagliato dalla foto dell'utente utilizzando l'input multimodale basato su dizionario (
[{"type": "text", ...}, {"type": "image", ...}]) egeneration_config={"thinking_level": "high", "temperature": 0.2}. - Nano Banana 2/2.1 (
gemini-3.1-flash-image or gemini-nano-banana-2.1): creazione di avatar di guida da immagine a immagine e di livree Gen4 utilizzando il prompt espanso. - Gemini Omni 1.1 Flash (
gemini-omni-1.1-flash-preview): generazione di video asincrona utilizzandobackground=Truee il pollingclient.interactions.get().
Non preoccuparti di rimanere bloccato Ogni esercizio di programmazione include un blocco 💡 Soluzione e spiegazione. Se riscontri problemi, scorri per visualizzare il codice completo e i dettagli dei parametri.
I passaggi di configurazione non relativi all'AI (scaffolding della shell, configurazioni Docker e deployment di Cloud Run) rimangono comandi di copia e incolla semplificati, in modo che tu possa concentrarti sulla padronanza dell'API Interactions.
Core Branding & Assets
Asset | Descrizione | URL di origine |
Auto di Formula E Gen4 | Riferimento ufficiale per l'auto da corsa completamente elettrica di 4ª generazione |
|
Logo Formula E x Google Cloud | Logo trasparente in co-branding |
|
Google Sans Regular Font | Tipografia ufficiale del brand |
|
Google Sans Medium Font | Tipografia ufficiale del brand |
|
2. 🛠️ Configurazione dell'ambiente e scaffolding del progetto
Creerai la directory dell'area di lavoro e i file di progetto direttamente in Cloud Shell utilizzando i comandi dell'interfaccia a riga di comando modulare.
1. Apri Cloud Shell e crea la directory del progetto
👉 Apri l'editor di Cloud Shell nel browser.
👉💻 Crea la struttura del workspace:
mkdir -p ~/fe-genmedia-kiosk/{backend/app/{routers,services,prompts},frontend/{public/fonts,src},scripts}
cd ~/fe-genmedia-kiosk
2. Abilita le API Google Cloud richieste
👉💻 Imposta l'ID progetto attuale e abilita i servizi richiesti:
export PROJECT_ID=$(gcloud config get-value project)
echo "Using Project ID: $PROJECT_ID"
gcloud services enable \
run.googleapis.com \
cloudbuild.googleapis.com \
aiplatform.googleapis.com \
storage.googleapis.com \
artifactregistry.googleapis.com \
compute.googleapis.com
3. Crea un service account e configura le autorizzazioni IAM
Nota: se utilizzi un account GCP dell'organizzazione e non puoi generare un service account, avrai invece un service account Compute Engine predefinito con autorizzazioni preconfigurate.
👉💻 Esegui i comandi di configurazione per il service account di runtime e Cloud Build:
# 1. Configure Cloud Build permissions for default Compute Service Account
# (Cloud Run source deployments use Cloud Build and the default Compute Engine SA)
export PROJECT_NUMBER=$(gcloud projects describe $PROJECT_ID --format="value(projectNumber)")
for role in roles/storage.admin roles/logging.logWriter roles/artifactregistry.writer; do
gcloud projects add-iam-policy-binding $PROJECT_ID \
--member="serviceAccount:${PROJECT_NUMBER}-compute@developer.gserviceaccount.com" \
--role="$role"
done
# 2. Create Cloud Storage Bucket
export BUCKET_NAME="fe-genmedia-kiosk-${PROJECT_ID}"
gcloud storage buckets create gs://${BUCKET_NAME} --location=us-central1
# 3. Configure CORS for web browser uploads
cat << 'EOF' > cors.json
[
{
"origin": ["*"],
"method": ["GET", "POST", "PUT", "DELETE", "HEAD"],
"responseHeader": ["*"],
"maxAgeSeconds": 3600
}
]
EOF
gcloud storage buckets update gs://${BUCKET_NAME} --cors-file=cors.json
4. Scarica gli asset del brand Formula E
👉💻 Recupera gli asset in co-branding nella tua cartella pubblica:
cd ~/fe-genmedia-kiosk/frontend/public
# Download Co-Branded Logo
curl -o formula_e_gc_logo.webp "https://storage.googleapis.com/fe-demo-branding/formula_e_gc_logo_transparent.webp"
# Download Gen4 Reference Car Image
curl -o gen4_car_reference.jpg "https://resources.formula-e.pulselive.com/photo-resources/2026/04/21/7c76418b-85af-492c-8ba6-81dabd6f3b4c/JAZ_3586-FE-2026-Gen4-Unleashed-JazzVian-for-ParcFerme.jpg?width=1280"
# Download Typography
curl -o fonts/GoogleSans-Regular.ttf "https://storage.googleapis.com/fe-demo-branding/GoogleSans-Regular.ttf"
curl -o fonts/GoogleSans-Medium.ttf "https://storage.googleapis.com/fe-demo-branding/GoogleSans-Medium.ttf"
cd ~/fe-genmedia-kiosk
5. Crea ambiente e configurazione delle dipendenze
👉💻 Crea .env e backend/pyproject.toml:
cat << EOF > .env
GOOGLE_CLOUD_PROJECT=${PROJECT_ID}
GOOGLE_CLOUD_LOCATION=global
GCS_BUCKET=${BUCKET_NAME}
GCS_SIGNING_SA=fe-kiosk-sa@${PROJECT_ID}.iam.gserviceaccount.com
EOF
cat << 'EOF' > backend/pyproject.toml
[project]
name = "fe-genmedia-backend"
version = "1.0.0"
description = "Formula E Generative Media Kiosk Backend"
dependencies = [
"fastapi>=0.110.0",
"uvicorn[standard]>=0.28.0",
"google-genai>=2.22.0",
"google-cloud-storage>=2.14.0",
"pydantic>=2.6.0",
"python-multipart>=0.0.9",
"pillow>=10.2.0",
"aiofiles>=23.2.0"
]
EOF
cat << 'EOF' > backend/app/config.py
import os
from pydantic import BaseModel
class Settings(BaseModel):
project_id: str = os.getenv("GOOGLE_CLOUD_PROJECT", "")
location: str = os.getenv("GOOGLE_CLOUD_LOCATION", "global")
gcs_bucket: str = os.getenv("GCS_BUCKET", "")
settings = Settings()
EOF
6. Servizio Gemini Seed Starter (backend/app/services/gemini_service.py)
👉💻 Crea il file del servizio di avvio contenente lo scaffolding e i gap TODO per le sfide pratiche e copia e incolla il codice di avvio:
touch backend/app/services/gemini_service.py
import base64
import json
import time
from google import genai
from google.genai import types
from google.cloud import storage
from app.config import settings
# =====================================================================
# TODO 1: Initialize the Google GenAI Client for Gemini Platform
# Use genai.Client() with enterprise=True, project, and location from settings.
# =====================================================================
client = None # REPLACE THIS WITH YOUR CODE
def upload_to_gcs(data_bytes: bytes, filename: str, content_type: str) -> str:
"""Uploads binary data to Cloud Storage bucket and returns public URL."""
storage_client = storage.Client()
bucket = storage_client.bucket(settings.gcs_bucket)
blob = bucket.blob(filename)
blob.upload_from_string(data_bytes, content_type=content_type)
return f"https://storage.googleapis.com/{settings.gcs_bucket}/{filename}"
def generate_driver_prompt(image_bytes: bytes, scene_option: str) -> str:
"""Uses Gemini 3.8 Flash via Interactions API to craft a detailed prompt from the driver photo."""
base64_image = base64.b64encode(image_bytes).decode("utf-8")
# =====================================================================
# TODO 2: Call client.interactions.create()
# Model: "gemini-3.8-flash"
# Input: list of dicts [{"type": "text", ...}, {"type": "image", "bytes": ..., "mime_type": "image/jpeg"}]
# Generation Config: {"thinking_level": "high", "temperature": 0.2}
# =====================================================================
interaction = None # REPLACE THIS WITH YOUR CODE
return interaction.output_text
def generate_driver_avatar(driver_prompt: str, image_bytes: bytes) -> bytes:
"""Uses Nano Banana 2 (gemini-3.1-flash-image) to generate stylized driver avatar."""
base64_image = base64.b64encode(image_bytes).decode("utf-8")
# =====================================================================
# TODO 3: Call client.interactions.create() for image generation
# Model: "gemini-3.1-flash-image"
# Input: list of dicts containing prompt text and input driver image
# Generation Config: {"aspect_ratio": "16:9", "output_mime_type": "image/png"}
# =====================================================================
interaction = None # REPLACE THIS WITH YOUR CODE
if interaction and interaction.output_image:
return base64.b64decode(interaction.output_image.data)
raise ValueError("Failed to generate avatar image.")
def generate_formula_e_video(driver_prompt: str, avatar_bytes: bytes) -> bytes:
"""Uses Gemini Omni Flash to generate an 8-second cinematic Formula E video."""
base64_avatar = base64.b64encode(avatar_bytes).decode("utf-8")
# =====================================================================
# TODO 4a: Start async video generation with 'gemini-omni-1.1-flash-preview'
# Input: list of dicts with avatar image and prompt text. Set background=True!
# =====================================================================
interaction = None # REPLACE THIS WITH YOUR CODE
# =====================================================================
# TODO 4b: Write polling loop with client.interactions.get(interaction.id)
# Loop every 5 seconds until status == "completed" or "failed".
# Return base64.b64decode(status_check.output_video.data)
# =====================================================================
while True:
# REPLACE THIS LOOP BODY WITH YOUR CODE
time.sleep(5)
3. ⚡ Implementazione dell'API Interactions e dei modelli generativi
In questa sezione, scriverai la logica di AI generativa di base in backend/app/services/gemini_service.py utilizzando l'API Interactions e l'API GenerateContent su Gemini Platform.
Apri backend/app/services/gemini_service.py nell'editor di Cloud Shell. Troverai quattro lacune della sfida TODO da completare.
Sfida 1: inizializza il client Gemini Enterprise
Apri backend/app/services/gemini_service.py. Completa TODO 1 inizializzando genai.Client configurato per Gemini Enterprise (enterprise=True).
import base64
import json
import time
from google import genai
from google.genai import types
from app.config import settings
# =====================================================================
# TODO 1: Initialize the Google GenAI Client for Gemini Platform
# Use genai.Client() with enterprise=True, project, and location from settings.
# =====================================================================
client = None # REPLACE THIS WITH YOUR CODE
💡 Ti serve un suggerimento o hai problemi? Scorri per visualizzare la soluzione per TODO 1
client = genai.Client(
enterprise=True,
project=settings.project_id,
location=settings.location
)
Sfida 2: espansione dei prompt del conducente con gemini-3.8-flash
Completa TODO 2 in generate_driver_prompt(). Utilizza gemini-3.8-flash per analizzare la foto del conducente e scrivere un prompt dettagliato per Nano Banana 2. Passa un elenco di dizionari a input e passa generation_config={"thinking_level": "high", "temperature": 0.2}.
def generate_driver_prompt(image_bytes: bytes, scene_option: str) -> str:
"""Uses Gemini 3.8 Flash via Interactions API to craft a detailed prompt from the driver photo."""
base64_image = base64.b64encode(image_bytes).decode("utf-8")
# =====================================================================
# TODO 2: Call client.interactions.create()
# Model: "gemini-3.8-flash"
# Input: list of dicts [{"type": "text", ...}, {"type": "image", "bytes": ..., "mime_type": "image/jpeg"}]
# Generation Config: {"thinking_level": "high", "temperature": 0.2}
# =====================================================================
interaction = None # REPLACE THIS WITH YOUR CODE
return interaction.output_text
💡 Ti serve un suggerimento o hai problemi? Fai clic per espandere la soluzione per TODO 2
interaction = client.interactions.create(
model="gemini-3.8-flash",
input=[
{"type": "text", "text": f"Analyze this photo and write a prompt describing this person as a Formula E race driver in a high-tech suit for a {scene_option} scene beside a Gen4 car."},
{"type": "image", "bytes": base64_image, "mime_type": "image/jpeg"}
],
generation_config={
"thinking_level": "high",
"temperature": 0.2
}
)
Sfida 3: generazione di avatar con Nano Banana 2 (gemini-3.1-flash-image tramite l'API GenerateContent)
Completa TODO 3 in generate_driver_avatar(). Chiama client.models.generate_content() con gemini-3.1-flash-image (Nano Banana 2) per eseguire il rendering dell'avatar di guida stilizzato del pilota di Formula E e dell'auto Gen4.
def generate_driver_avatar(driver_prompt: str, image_bytes: bytes) -> bytes:
"""Uses Nano Banana 2 (gemini-3.1-flash-image) to generate stylized driver avatar."""
text_parts = types.Part.from_text(text=f"Generate a stylized Formula E driver portrait: {driver_prompt}")
image_parts = types.Part.from_bytes(data=image_bytes,
mime_type='image/jpeg',)
gen4_image_parts = types.Part.from_uri(file_uri="https://coffeecornermotorsport.com/wp-content/uploads/2026/04/m37258_GEN4-HERO3.webp", mime_type="image/webp")
# =====================================================================
# TODO 3: Call client.models.generate_content() for image generation
# Model: "gemini-3.1-flash-image" or "gemini-nano-banana-2.1"
# Content: A list with the parts
# Generation Config: Image Response Modality and 16:9 Aspect ratio
# =====================================================================
response = None # REPLACE THIS WITH YOUR CODE
generated_bytes = None
if response.candidates and response.candidates[0].content.parts:
for part in response.candidates[0].content.parts:
if part.inline_data and part.inline_data.data:
generated_bytes = part.inline_data.data
break
return base64.b64encode(generated_bytes).decode('utf-8')
💡 Ti serve un suggerimento o hai problemi? Scorri per visualizzare la soluzione per TODO 3
response = client.models.generate_content(
model="gemini-3.1-flash-image", # Or "gemini-nano-banana-2.1"
content=[text_parts, image_parts, gen4_image_parts],
config=types.GenerateContentConfig(
response_modalities=["IMAGE"],
image_config=types.ImageConfig(aspect_ratio="16:9"),
)
)
Sfida 4: generazione di video con Gemini Omni 1.1 Flash
Completa TODO 4 in generate_formula_e_video(). Avvia un'interazione video asincrona con gemini-omni-1.1-flash-preview utilizzando background=True, esegui il polling di client.interactions.get() finché lo stato non è "completato" e restituisci i byte del video decodificati in base64 (output_video.data).
def generate_formula_e_video(driver_prompt: str, avatar_bytes: bytes) -> bytes:
"""Uses Gemini Omni 1.1 Flash to generate an 8-second cinematic Formula E video."""
base64_avatar = base64.b64encode(avatar_bytes).decode("utf-8")
# =====================================================================
# TODO 4a: Start async video generation with 'gemini-omni-1.1-flash-preview'
# Input: list of dicts with avatar image and prompt text. Set background=True!
# =====================================================================
interaction = None # REPLACE THIS WITH YOUR CODE
# =====================================================================
# TODO 4b: Write polling loop with client.interactions.get(interaction.id)
# Loop every 5 seconds until status == "completed" or "failed".
# Return base64.b64decode(status_check.output_video.data)
# =====================================================================
while True:
# REPLACE THIS LOOP BODY WITH YOUR CODE
time.sleep(5)
💡 Ti serve un suggerimento o hai problemi? Fai clic per espandere la soluzione per TODO 4
# TODO 4a Solution:
interaction = client.interactions.create(
model="gemini-omni-1.1-flash-preview",
input=[
{"type": "text", "text": f"Generate an 8-second 60fps cinematic video: {driver_prompt}. Formula E racing atmosphere."},
{"type": "image", "bytes": base64_avatar, "mime_type": "image/png"}
],
background=True
)
# TODO 4b Solution:
while True:
status_check = client.interactions.get(interaction.id)
if status_check.status == "completed":
return base64.b64decode(status_check.output_video.data)
elif status_check.status in ["failed", "cancelled"]:
raise RuntimeError(f"Video generation failed: {status_check.status}")
time.sleep(5)
4. 🏗️ Assemblaggio dell'applicazione principale e del frontend
Ora crea i restanti file wrapper dell'applicazione, gli endpoint API e l'interfaccia React.
[!IMPORTANT] Assicurati di eseguire tutti i comandi di creazione dei file in questa sezione dalla directory root dell'area di lavoro (~/fe-genmedia-kiosk).
👉💻 Vai alla directory root del progetto:
cd ~/fe-genmedia-kiosk
1. Applicazione FastAPI principale (backend/app/main.py)
👉💻 Crea backend/app/main.py e copia/incolla il codice Python riportato di seguito:
touch backend/app/main.py
from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import asyncio
import uuid
from app.services.gemini_service import (
generate_driver_prompt,
generate_driver_avatar,
generate_formula_e_video,
upload_to_gcs
)
app = FastAPI(title="Formula E AI Kiosk API")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
jobs_db = {}
class JobStatus(BaseModel):
job_id: str
status: str
stage: str
result_url: str | None = None
@app.get("/api/health")
def health_check():
return {"status": "ok", "experience": "Formula E Gen4 AI Kiosk"}
@app.post("/api/process-driver")
async def process_driver(
file: UploadFile = File(...),
option: str = Form("podium_celebration")
):
job_id = str(uuid.uuid4())
image_bytes = await file.read()
jobs_db[job_id] = {
"status": "processing",
"stage": "generating_prompt",
"result_url": None
}
asyncio.create_task(run_kiosk_pipeline(job_id, image_bytes, option))
return {"job_id": job_id, "status": "queued"}
async def run_kiosk_pipeline(job_id: str, image_bytes: bytes, option: str):
try:
jobs_db[job_id]["stage"] = "Writing driver prompt with Gemini 3.8 Flash..."
driver_prompt = generate_driver_prompt(image_bytes, option)
jobs_db[job_id]["stage"] = "Rendering Nano Banana 2 avatar..."
avatar_bytes = generate_driver_avatar(driver_prompt, image_bytes)
jobs_db[job_id]["stage"] = "Generating Gemini Omni Flash video..."
video_bytes = generate_formula_e_video(driver_prompt, avatar_bytes)
jobs_db[job_id]["stage"] = "Publishing generated video to Cloud Storage..."
result_url = upload_to_gcs(video_bytes, f"videos/{job_id}.mp4", "video/mp4")
jobs_db[job_id]["status"] = "completed"
jobs_db[job_id]["stage"] = "Done"
jobs_db[job_id]["result_url"] = result_url
except Exception as e:
jobs_db[job_id]["status"] = "failed"
jobs_db[job_id]["stage"] = str(e)
@app.get("/api/job/{job_id}", response_model=JobStatus)
def get_job_status(job_id: str):
if job_id not in jobs_db:
raise HTTPException(status_code=404, detail="Job not found")
data = jobs_db[job_id]
return JobStatus(
job_id=job_id,
status=data["status"],
stage=data["stage"],
result_url=data.get("result_url")
)
2. Dockerfile di backend (backend/Dockerfile)
👉💻 Crea la configurazione del container di backend:
cat << 'EOF' > backend/Dockerfile
FROM python:3.11-slim
RUN apt-get update && apt-get install -y ffmpeg libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY pyproject.toml .
RUN pip install --no-cache-dir .
COPY app/ ./app/
EXPOSE 8080
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]
EOF
3. Applicazione e configurazione React frontend
Crea i file di configurazione e di origine del frontend passo dopo passo per assicurarti che ogni file venga generato correttamente in Cloud Shell senza problemi di buffer di incollamento.
3.1 Manifest del pacchetto (frontend/package.json)
👉💻 Crea la definizione delle dipendenze del frontend:
cat << 'EOF' > frontend/package.json
{
"name": "fe-genmedia-frontend",
"private": true,
"version": "1.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "tsc && vite build",
"preview": "vite preview"
},
"dependencies": {
"react": "^18.2.0",
"react-dom": "^18.2.0"
},
"devDependencies": {
"@types/react": "^18.2.66",
"@types/react-dom": "^18.2.22",
"@vitejs/plugin-react": "^4.2.1",
"typescript": "^5.2.2",
"vite": "^5.1.6"
}
}
EOF
3.2 Configurazione di TypeScript (frontend/tsconfig.json)
👉💻 Crea frontend/tsconfig.json in modo che il compilatore TypeScript (tsc) possa convalidare e creare il progetto:
cat << 'EOF' > frontend/tsconfig.json
{
"compilerOptions": {
"target": "ES2020",
"useDefineForClassFields": true,
"lib": ["ES2020", "DOM", "DOM.Iterable"],
"module": "ESNext",
"skipLibCheck": true,
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"resolveJsonModule": true,
"isolatedModules": true,
"noEmit": true,
"jsx": "react-jsx",
"strict": false
},
"include": ["src"]
}
EOF
3.3 Configurazione di compilazione Vite (frontend/vite.config.ts)
👉💻 Crea la configurazione di Vite:
cat << 'EOF' > frontend/vite.config.ts
import { defineConfig } from 'vite';
import react from '@vitejs/plugin-react';
export default defineConfig({
plugins: [react()],
server: {
port: 8080,
host: true
}
});
EOF
3.4 Entry point HTML (frontend/index.html)
👉💻 Crea il punto di contatto HTML principale:
cat << 'EOF' > frontend/index.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Formula E AI Kiosk</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>
EOF
3.5 Foglio di stile del brand (frontend/src/index.css)
👉💻 Crea il tema personalizzato della Formula E in modalità Buio:
cat << 'EOF' > frontend/src/index.css
@font-face {
font-family: 'Google Sans';
src: url('/fonts/GoogleSans-Regular.ttf') format('truetype');
font-weight: 400;
}
@font-face {
font-family: 'Google Sans';
src: url('/fonts/GoogleSans-Medium.ttf') format('truetype');
font-weight: 500;
}
body {
margin: 0;
font-family: 'Google Sans', sans-serif;
background-color: #0b0e14;
color: #ffffff;
}
.fe-container {
max-width: 1200px;
margin: 0 auto;
padding: 2rem;
text-align: center;
}
.fe-header {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1rem 2rem;
background: rgba(15, 23, 42, 0.8);
border-bottom: 2px solid #00e5ff;
}
.fe-btn {
background: linear-gradient(135deg, #00e5ff 0%, #0066ff 100%);
color: #000;
font-weight: bold;
padding: 0.8rem 2rem;
border: none;
border-radius: 30px;
cursor: pointer;
font-size: 1.1rem;
transition: all 0.2s ease;
}
.fe-btn:hover:not(:disabled) {
transform: translateY(-2px);
box-shadow: 0 4px 15px rgba(0, 229, 255, 0.4);
}
.fe-btn:disabled {
opacity: 0.45;
cursor: not-allowed;
}
.fe-btn-secondary {
background: rgba(30, 41, 59, 0.8);
color: #00e5ff;
border: 1px solid #00e5ff;
font-weight: bold;
padding: 0.6rem 1.6rem;
border-radius: 30px;
cursor: pointer;
font-size: 1rem;
transition: all 0.2s ease;
}
.fe-btn-secondary:hover:not(:disabled) {
background: rgba(0, 229, 255, 0.15);
box-shadow: 0 0 10px rgba(0, 229, 255, 0.3);
}
.fe-card {
background: #151c28;
border: 1px solid #1e293b;
border-radius: 16px;
padding: 2rem;
margin: 1.5rem auto;
max-width: 720px;
}
.fe-tabs {
display: flex;
justify-content: center;
gap: 1rem;
margin-bottom: 1.5rem;
}
.fe-tab-btn {
background: #0f172a;
border: 1px solid #334155;
color: #94a3b8;
font-size: 1rem;
font-weight: 500;
padding: 0.6rem 1.4rem;
border-radius: 20px;
cursor: pointer;
transition: all 0.2s ease;
}
.fe-tab-btn.active {
background: #1e293b;
border-color: #00e5ff;
color: #00e5ff;
box-shadow: 0 0 10px rgba(0, 229, 255, 0.2);
}
.fe-camera-container {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
margin: 1.5rem auto;
}
.fe-camera-feed-box {
position: relative;
width: 100%;
max-width: 480px;
aspect-ratio: 4/3;
background: #0b0e14;
border-radius: 12px;
overflow: hidden;
border: 2px solid #334155;
display: flex;
align-items: center;
justify-content: center;
}
.fe-video {
width: 100%;
height: 100%;
object-fit: cover;
transform: scaleX(-1);
}
.fe-captured-img {
width: 100%;
height: 100%;
object-fit: cover;
border-radius: 12px;
}
.fe-alert {
background: rgba(239, 68, 68, 0.15);
border: 1px solid #ef4444;
color: #fca5a5;
padding: 0.8rem 1.2rem;
border-radius: 8px;
margin: 1rem auto;
max-width: 480px;
font-size: 0.95rem;
}
.fe-file-picker {
margin: 1.5rem 0;
}
.fe-file-picker input[type="file"] {
background: #0f172a;
border: 1px dashed #334155;
padding: 1.5rem;
border-radius: 12px;
color: #94a3b8;
width: 80%;
max-width: 400px;
cursor: pointer;
}
.fe-file-picker input[type="file"]:hover {
border-color: #00e5ff;
color: #ffffff;
}
EOF
3.6 Componente principale di React (frontend/src/main.tsx)
👉💻 Crea il file di bootstrap dell'applicazione React:
cat << 'EOF' > frontend/src/main.tsx
import React from 'react';
import ReactDOM from 'react-dom/client';
import App from './App';
import './index.css';
ReactDOM.createRoot(document.getElementById('root')!).render(
<React.StrictMode>
<App />
</React.StrictMode>,
);
EOF
3.7 UI del chiosco interattivo (frontend/src/App.tsx)
👉💻 Crea l'interfaccia interattiva principale del kiosk creando App.tsx e copiando e incollando il codice React:
touch frontend/src/App.tsx
import React, { useState, useRef, useEffect, useCallback } from 'react';
import './index.css';
export default function App() {
const [inputMode, setInputMode] = useState<'camera' | 'upload'>('camera');
const [file, setFile] = useState<File | null>(null);
const [previewUrl, setPreviewUrl] = useState<string | null>(null);
const [cameraActive, setCameraActive] = useState<boolean>(false);
const [cameraError, setCameraError] = useState<string | null>(null);
const [option, setOption] = useState('podium_celebration');
const [jobId, setJobId] = useState<string | null>(null);
const [status, setStatus] = useState<string>('');
const [stage, setStage] = useState<string>('');
const [resultUrl, setResultUrl] = useState<string | null>(null);
const videoRef = useRef<HTMLVideoElement | null>(null);
const canvasRef = useRef<HTMLCanvasElement | null>(null);
const streamRef = useRef<MediaStream | null>(null);
const stopCamera = useCallback(() => {
if (streamRef.current) {
streamRef.current.getTracks().forEach((track) => track.stop());
streamRef.current = null;
}
if (videoRef.current) {
videoRef.current.srcObject = null;
}
setCameraActive(false);
}, []);
const startCamera = useCallback(async () => {
stopCamera();
setCameraError(null);
try {
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
throw new Error('Camera is not supported on this browser or device.');
}
const stream = await navigator.mediaDevices.getUserMedia({
video: {
width: { ideal: 1280 },
height: { ideal: 720 },
facingMode: 'user',
},
audio: false,
});
streamRef.current = stream;
if (videoRef.current) {
videoRef.current.srcObject = stream;
await videoRef.current.play();
}
setCameraActive(true);
} catch (err: any) {
console.error('Error accessing camera:', err);
setCameraError(err.message || 'Unable to access camera. Please check permissions or switch to file upload.');
setCameraActive(false);
}
}, [stopCamera]);
useEffect(() => {
if (inputMode === 'camera' && !file && !jobId) {
startCamera();
} else {
stopCamera();
}
return () => {
stopCamera();
};
}, [inputMode, file, jobId, startCamera, stopCamera]);
const handleCapture = () => {
const video = videoRef.current;
const canvas = canvasRef.current;
if (!video || !canvas) return;
const width = video.videoWidth || 640;
const height = video.videoHeight || 480;
canvas.width = width;
canvas.height = height;
const ctx = canvas.getContext('2d');
if (!ctx) return;
// Flip horizontally to match selfie preview
ctx.translate(width, 0);
ctx.scale(-1, 1);
ctx.drawImage(video, 0, 0, width, height);
canvas.toBlob((blob) => {
if (!blob) return;
const capturedFile = new File([blob], `driver_photo_${Date.now()}.jpg`, {
type: 'image/jpeg',
});
setFile(capturedFile);
setPreviewUrl(URL.createObjectURL(blob));
stopCamera();
}, 'image/jpeg', 0.95);
};
const handleRetake = () => {
if (previewUrl) {
URL.revokeObjectURL(previewUrl);
}
setFile(null);
setPreviewUrl(null);
if (inputMode === 'camera') {
startCamera();
}
};
const handleFileChange = (e: React.ChangeEvent<HTMLInputElement>) => {
const selected = e.target.files?.[0] || null;
if (selected) {
setFile(selected);
if (previewUrl) {
URL.revokeObjectURL(previewUrl);
}
setPreviewUrl(URL.createObjectURL(selected));
}
};
const handleModeChange = (mode: 'camera' | 'upload') => {
if (mode === inputMode) return;
if (previewUrl) {
URL.revokeObjectURL(previewUrl);
}
setFile(null);
setPreviewUrl(null);
setInputMode(mode);
};
const handleUpload = async () => {
if (!file) return;
stopCamera();
const formData = new FormData();
formData.append('file', file);
formData.append('option', option);
const apiBase = (import.meta as any).env.VITE_API_BASE || '';
const res = await fetch(`${apiBase}/api/process-driver`, { method: 'POST', body: formData });
const data = await res.json();
setJobId(data.job_id);
pollJob(data.job_id);
};
const pollJob = (id: string) => {
const apiBase = (import.meta as any).env.VITE_API_BASE || '';
const interval = window.setInterval(async () => {
try {
const res = await fetch(`${apiBase}/api/job/${id}`);
const data = await res.json();
setStatus(data.status);
setStage(data.stage);
if (data.status === 'completed') {
setResultUrl(data.result_url);
clearInterval(interval);
} else if (data.status === 'failed') {
clearInterval(interval);
}
} catch (err) {
console.error('Error polling job:', err);
}
}, 3000);
};
const handleReset = () => {
if (previewUrl) {
URL.revokeObjectURL(previewUrl);
}
setFile(null);
setPreviewUrl(null);
setJobId(null);
setStatus('');
setStage('');
setResultUrl(null);
};
return (
<div>
<header className="fe-header">
<img src="/formula_e_gc_logo.webp" alt="Formula E Google Cloud" height="45" />
<h2>GEN4 AI DRIVER EXPERIENCE</h2>
</header>
<main className="fe-container">
<h1>Transform into a Formula E Driver</h1>
<p>Powered by Gemini Platform & Google Cloud Run</p>
{!jobId ? (
<div className="fe-card">
<h3>Step 1: Take or Upload Your Driver Photo</h3>
<div className="fe-tabs">
<button
type="button"
className={`fe-tab-btn ${inputMode === 'camera' ? 'active' : ''}`}
onClick={() => handleModeChange('camera')}
>
📷 Take Photo
</button>
<button
type="button"
className={`fe-tab-btn ${inputMode === 'upload' ? 'active' : ''}`}
onClick={() => handleModeChange('upload')}
>
📁 Upload Photo
</button>
</div>
{/* Mode: Camera */}
{inputMode === 'camera' && (
<div className="fe-camera-container">
{cameraError && (
<div className="fe-alert">
<p>{cameraError}</p>
<button type="button" className="fe-btn-secondary" onClick={startCamera} style={ { marginTop: '0.5rem' } }>
Try Camera Again
</button>
</div>
)}
<div className="fe-camera-feed-box">
{!file ? (
<video
ref={videoRef}
className="fe-video"
autoPlay
playsInline
muted
/>
) : (
previewUrl && (
<img src={previewUrl} alt="Captured Driver" className="fe-captured-img" />
)
)}
</div>
<canvas ref={canvasRef} style={ { display: 'none' } } />
<div style={ { marginTop: '1rem', display: 'flex', gap: '0.75rem', justifyContent: 'center' } }>
{!file ? (
<button
type="button"
className="fe-btn"
onClick={handleCapture}
disabled={!cameraActive}
>
📸 Snap Photo
</button>
) : (
<button
type="button"
className="fe-btn-secondary"
onClick={handleRetake}
>
🔄 Retake Photo
</button>
)}
</div>
</div>
)}
{/* Mode: Upload */}
{inputMode === 'upload' && (
<div className="fe-file-picker">
{!file ? (
<input
type="file"
accept="image/*"
onChange={handleFileChange}
/>
) : (
<div>
{previewUrl && (
<div className="fe-camera-feed-box" style={ { margin: '0 auto 1rem auto' } }>
<img src={previewUrl} alt="Uploaded Driver" className="fe-captured-img" />
</div>
)}
<button type="button" className="fe-btn-secondary" onClick={handleRetake}>
Choose Different Photo
</button>
</div>
)}
</div>
)}
<h3>Step 2: Choose Your Experience</h3>
<select
value={option}
onChange={(e) => setOption(e.target.value)}
style={ { padding: '0.6rem 1rem', fontSize: '1rem', background: '#0f172a', color: '#fff', border: '1px solid #334155', borderRadius: '8px' } }
>
<option value="podium_celebration">🏆 Podium Winner Celebration</option>
<option value="pit_lane_launch">⚡ Pit Lane Gen4 Launch</option>
</select>
<div style={ { marginTop: '2rem' } }>
<button className="fe-btn" onClick={handleUpload} disabled={!file}>
START GENERATIVE PIPELINE
</button>
</div>
</div>
) : (
<div className="fe-card">
<h2>Pipeline Progress</h2>
<p><strong>Status:</strong> {status.toUpperCase() || 'INITIALIZING...'}</p>
<p><strong>Stage:</strong> {stage || 'Starting processing...'}</p>
{resultUrl && (
<div style={ { marginTop: '2rem' } }>
<h3>Your Formula E Generative Video</h3>
<video src={resultUrl} controls autoPlay loop style={ { width: '100%', maxWidth: '720px', borderRadius: '12px' } } />
</div>
)}
{(status === 'completed' || status === 'failed') && (
<div style={ { marginTop: '2rem' } }>
<button type="button" className="fe-btn-secondary" onClick={handleReset}>
CREATE ANOTHER DRIVER
</button>
</div>
)}
</div>
)}
</main>
</div>
);
}
3.8 Configurazione del server Nginx (frontend/nginx.conf)
👉💻 Crea la configurazione del server web Nginx per il routing dell'applicazione a pagina singola:
cat << 'EOF' > frontend/nginx.conf
server {
listen 8080;
location / {
root /usr/share/nginx/html;
index index.html;
try_files $uri $uri/ /index.html;
}
}
EOF
3.9 Configurazione della build del container di frontend (frontend/Dockerfile e frontend/.dockerignore)
👉💻 Crea .dockerignore e la produzione multifase Dockerfile:
cat << 'EOF' > frontend/.dockerignore
node_modules
dist
.git
EOF
touch frontend/Dockerfile
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json tsconfig.json ./
RUN npm install
COPY . .
RUN npm run build
FROM nginx:alpine
COPY --from=builder /app/dist /usr/share/nginx/html
COPY nginx.conf /etc/nginx/conf.d/default.conf
EXPOSE 8080
5. 🚀 Deployment in Cloud Run
1. Crea ed esegui il deployment del container di backend
👉💻 Esegui il deployment del server di backend su Cloud Run:
cd ~/fe-genmedia-kiosk/backend
gcloud run deploy fe-kiosk-backend \
--source . \
--region us-central1 \
--allow-unauthenticated \
--port 8080
2. Crea ed esegui il deployment del container di frontend
Una volta completato il backend, acquisisci il suo URL, scrivilo direttamente in .env.production ed esegui il deployment del frontend
cd ~/fe-genmedia-kiosk/frontend
# 1. Fetch Backend URL
export BACKEND_URL=$(gcloud run services describe fe-kiosk-backend \
--region us-central1 \
--format="value(status.url)")
echo "Configuring frontend with Backend URL: $BACKEND_URL"
# 2. Write it into the Vite production environment file
echo "VITE_API_BASE=${BACKEND_URL}" > .env.production
# 3. Deploy frontend (Vite will read .env.production during build)
gcloud run deploy fe-kiosk-frontend \
--source . \
--region us-central1 \
--allow-unauthenticated \
--port 8080
6. 🏁 Test e utilizzo del kiosk AI di Formula E
- Apri
$FRONTEND_URLin Google Chrome. - Carica un selfie o un ritratto.
- Seleziona lo scenario di gara (Podium Winner Celebration o Pit Lane Gen4 Launch).
- Fai clic su AVVIA PIPELINE GENERATIVA.
- Osserva i progressi in tempo reale in tutte e tre le fasi dell'AI:
- Gemini 3.8 Flash: generazione di prompt per i driver.
- Nano Banana 2: rendering di avatar personalizzati di auto e tute da corsa Gen4.
- Gemini Omni Flash: generazione di video HD di 8 secondi a 60 fps.
- Riproduci il video generativo finale.
7. 🎉 Conclusion & Clean-up
Che cosa hai imparato
✅ Piattaforma Gemini e API Interactions: inizializzazione di genai.Client(enterprise=True) e chiamata di client.interactions.create().
✅ Schema dei dati multimodali dell'API Interactions: utilizzo di list-of-dicts input e del parametro dict generation_config.
✅ Pipeline di AI multimodale:
gemini-3.8-flash: generazione di prompt personalizzati per i conducenti.gemini-3.1-flash-image(Nano Banana 2): creazione di avatar dei piloti e livree Gen4.gemini-omni-1.1-flash-preview: generazione di video asincrona con polling delle attività in background.
✅ Deployment serverless: containerizzazione e deployment dei servizi su Google Cloud Run.
Pulizia delle risorse
👉💻 Pulisci il tuo ambiente Google Cloud:
gcloud run services delete fe-kiosk-backend --region us-central1 --quiet
gcloud run services delete fe-kiosk-frontend --region us-central1 --quiet
gcloud storage rm -r gs://${BUCKET_NAME}
gcloud iam service-accounts delete fe-kiosk-sa@${PROJECT_ID}.iam.gserviceaccount.com --quiet