AI‑Augmented‑Life‑Coach‑On‑Your‑Phone‑Vision‑Mode
in three sprints!
(All upbeat, bite‑sized, and action‑ready—because momentum is magic.)
🏃♂️ Sprint 1 – Core prototype (one weekend)
| Piece | What you’ll do | Why it matters |
| 1. Camera feed | Use Expo + React Native or Flutter; open the rear camera at 720 p / 5 fps to keep bandwidth tiny. | Smooth, low‑latency frames from any iOS/Android phone. |
| 2. Lightweight backend | Spin up a FastAPI (or Flask) micro‑service on Render / Fly.io. Endpoint /analyze accepts a base64 JPEG. | Keeps your OpenAI key off the device, lets you hot‑swap logic. |
| 3. Vision call | Inside the endpoint, call gpt‑4o‑vision-preview with the user’s frame + your coaching prompt (see code). | GPT‑4o does the heavy lifting—composes, critiques, motivates in one shot. |
| 4. Overlay the advice | Return 1‑2 sentences. Front‑end draws translucent text (or speaks with TTS). | Real‑time “coach in your ear.” |
Backend snippet (FastAPI, Python >= 3.12)
from fastapi import FastAPI, UploadFile
from openai import OpenAI
import base64, io, uvicorn
client = OpenAI() # uses OPENAI_API_KEY env var
app = FastAPI()
SYSTEM_PROMPT = (
“You are Street‑Photo‑Coach. “
“For each image: 1) Identify main compositional issue, “
“2) Give ONE bold action (imperative), “
“3) Finish with 5‑word hype mantra.”
)
@app.post(“/analyze”)
async def analyze(file: UploadFile):
img_bytes = await file.read()
img_b64 = base64.b64encode(img_bytes).decode()
response = client.chat.completions.create(
model=”gpt-4o-vision-preview”,
max_tokens=80,
messages=[
{“role”: “system”, “content”: SYSTEM_PROMPT},
{“role”: “user”, “content”: [
{“type”: “text”, “text”: “Critique and coach:”},
{“type”: “image_url”,
“image_url”: {“url”: f”data:image/jpeg;base64,{img_b64}”}}
]}
]
)
return {“feedback”: response.choices[0].message.content.strip()}
Deploy, grab the auto‑generated URL, and you’re live! 🚀
🏃♀️ Sprint 2 – Level‑up features (one week)
- Tap‑to‑freeze: Let users pause the stream, scribble notes, then resume.
- Skill modes: “Beginner / Intermediate / Pro”—adjust prompt weight (“explain more” vs “just one tweak”).
- Offline queue: Cache frames when 4G drops (handy in Phnom Penh side streets), batch‑send later to save tokens.
- Vocal hype: Use Expo’s Speech.speak() or iOS AVSpeech to read the feedback aloud—eyes stay on the scene.
- Privacy toggle: On device, blur faces with an MLKit filter before upload if the user wants anonymity.
🛠 Sprint 3 – Polished product (ongoing)
- Generative drills: Ask GPT‑4o to craft mini‑missions (“Shoot 3 reflections in 10 min—GO!”).
- Vision+Voice combo: When OpenAI voice endpoints fully integrate (per GPT‑4o roadmap) you can stream audio questions (“Coach, am I too far?”) and receive instant spoken answers.
- Model‑switch dial: Fall back to gpt‑4o-mini for low‑cost sessions; upgrade to gpt‑4o for workshop clients.
- Community layer: Pipe shots + AI feedback into a private Discord or your own Arsβeta‑style board for hybrid human/machine critique.
Cost & latency tips
| Trick | Win |
| Down‑scale to 640 px wide | ~90 KB/frame ⇒ cheap tokens & fast uploads. |
| Send every 2 s, not every frame | Human reaction time sweet spot. |
| Cache identical feedback | If GPT’s answer repeats, skip a billable call. |
| Use JSON mode | Smaller payload, easier parsing. |
Your next step (today!)
- Clone a blank Expo starter (npx create-expo-app vision‑coach).
- Copy the FastAPI snippet → Deploy free tier.
- Wire fetch(‘/analyze’, formData) on each interval.
- Watch the phone shout:
“Move closer! Fill the frame! — SHOOT WITH HEART!”
That’s it—prototype in a weekend, momentum for life. Go unleash those bold frames and let your pocket coach cheer you all the way to photographic greatness. 📸🔥