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from fastapi import FastAPI, File, UploadFile, Form
from fastapi.middleware.cors import CORSMiddleware
import json
import tempfile
import shutil
import os
from single_person_processor import SinglePersonPredictor
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:3000"], # Change this to match your frontend URL
allow_credentials=True,
allow_methods=["*"], # Allow all methods (POST, GET, etc.)
allow_headers=["*"], # Allow all headers
)
@app.get("/")
async def root():
return {"message": "FastAPI is running! Use /docs for API documentation."}
# Initialize the predictor
predictor = SinglePersonPredictor(model_path="best_model.keras")
@app.post("/predict/")
async def predict(
front_image: UploadFile = File(...),
side_image: UploadFile = File(...),
input_data: str = Form(...)
):
try:
try:
input_dict = json.loads(input_data)
except json.JSONDecodeError:
return {"error": "Invalid JSON format in input_data"}
# Validate file types
if front_image.content_type not in ["image/jpeg", "image/png"]:
return {"error": "Front image must be a JPEG or PNG file"}
if side_image.content_type not in ["image/jpeg", "image/png"]:
return {"error": "Side image must be a JPEG or PNG file"}
# Create temporary files for images
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as front_temp:
shutil.copyfileobj(front_image.file, front_temp)
front_temp_path = front_temp.name
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as side_temp:
shutil.copyfileobj(side_image.file, side_temp)
side_temp_path = side_temp.name
# Perform predictions
results = predictor.predict_measurements(
front_img_path=front_temp_path,
side_img_path=side_temp_path,
gender=input_dict.get("gender"),
height_cm=input_dict.get("height_cm"),
weight_kg=input_dict.get("weight_kg"),
apparel_type=input_dict.get("apparel_type")
)
# Clean up temporary files
os.remove(front_temp_path)
os.remove(side_temp_path)
return {"results": results}
except Exception as e:
return {"error": str(e)}
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