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d3776ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # FastAPI backend to receive video chunks from React frontend
# and save them as .webm files on Windows
#
# Install dependencies:
# pip install fastapi uvicorn python-multipart
#
# for VITE_BACKEND_URL=http://localhost:8000
#
# Run:
# uvicorn test:app --reload --port 8000
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
import os
from datetime import datetime
# ── Folder setup ──────────────────────────────────────────────────────────────
# This creates a "frames" folder in the same directory as main.py
SAVE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "frames")
# Create the folder if it doesn't exist yet
os.makedirs(SAVE_DIR, exist_ok=True)
# ── Lifespan (replaces deprecated @app.on_event) ──────────────────────────────
@asynccontextmanager
async def lifespan(app: FastAPI):
# Runs on startup
print(" FastAPI is running on http://localhost:8000")
print(f" Saving frames to: {SAVE_DIR}")
yield
# Runs on shutdown
print("─" * 50)
# ── App setup ────────────────────────────────────────────────────────────────
app = FastAPI(lifespan=lifespan)
# Allow React frontend (Vite runs on 5173 by default) to talk to this backend
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# ── Route ─────────────────────────────────────────────────────────────────────
@app.post("/api/detect")
async def detect(chunk: UploadFile = File(...)):
"""
Receives a video chunk (blob) from the React frontend every 2 seconds.
Saves it as a .webm file inside the frames/ folder.
"""
# Read raw bytes from the uploaded blob
blob_bytes = await chunk.read()
# ── Validation ────────────────────────────────────────────────────────────
# Log what we received in the terminal
print(f"[RECEIVED] size: {len(blob_bytes)} bytes | type: {chunk.content_type}")
# Reject empty chunks
if len(blob_bytes) == 0:
print("[ERROR] Empty chunk — skipping save")
return JSONResponse(
content={"error": "Empty chunk received"},
status_code=400
)
# ── Save file ─────────────────────────────────────────────────────────────
# Use timestamp in filename so chunks don't overwrite each other
# Example filename: chunk_20241201_123001_456789.webm
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
filename = f"chunk_{timestamp}.webm"
# os.path.join works correctly on Windows
save_path = os.path.join(SAVE_DIR, filename)
# Write bytes to disk
with open(save_path, "wb") as f:
f.write(blob_bytes)
# Confirm the file actually landed on disk and get its size
saved_size = os.path.getsize(save_path)
print(f"[SAVED] {save_path} ({saved_size} bytes)")
# ── Response ──────────────────────────────────────────────────────────────
# Feed the saved webm to your model to process
# Replace this with your actual model inference (this is a dummy response)
return JSONResponse(content={
"label": "REAL", # "FAKE" or "REAL"
"confidence": 0.99, # float between 0.0 and 1.0
})
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