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import os
os.environ["HF_HOME"] = "/tmp/huggingface"
from fastapi import FastAPI, UploadFile, File
from transformers import SiglipForImageClassification, AutoImageProcessor
from PIL import Image
import torch
import torch.nn.functional as F
import io
from typing import List
app = FastAPI()
model_name = "prithivMLmods/Gender-Classifier-Mini"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
@app.get("/")
async def root():
return {"message": "Gender classifier API is running. Use POST /classify/ with an image file."}
@app.post("/classify/")
async def classify_gender(image: UploadFile = File(...)):
contents = await image.read()
try:
img = Image.open(io.BytesIO(contents)).convert("RGB")
except Exception:
return {"error": "Invalid image file"}
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=1).squeeze().tolist()
labels = ["Female ♀", "Male ♂"]
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
max_idx = probs.index(max(probs))
return {
"predictions": predictions,
"most_likely": labels[max_idx],
"confidence": round(probs[max_idx], 3)
}
@app.post("/classify_batch/")
async def classify_gender_batch(images: List[UploadFile] = File(...)):
pil_images = []
for image in images:
contents = await image.read()
try:
img = Image.open(io.BytesIO(contents)).convert("RGB")
pil_images.append(img)
except Exception:
return {"error": f"Invalid image file: {image.filename}"}
# Batch process
inputs = processor(images=pil_images, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=1).tolist() # shape: [batch_size, 2]
labels = ["Female ♀", "Male ♂"]
results = []
for p in probs:
predictions = {labels[i]: round(p[i], 3) for i in range(len(p))}
max_idx = p.index(max(p))
results.append({
"predictions": predictions,
"most_likely": labels[max_idx],
"confidence": round(p[max_idx], 3)
})
return {"results": results}
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