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# app.py
import os
os.environ["HF_HOME"] = "/tmp/huggingface"
os.makedirs("/tmp/huggingface", exist_ok=True)
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from PIL import Image
import torch
from transformers import AutoImageProcessor, SiglipForImageClassification
from io import BytesIO
app = FastAPI(title="Alphabet Sign Language Detection API")
# Load model and processor once (to speed up inference)
model_name = "prithivMLmods/Alphabet-Sign-Language-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label map
labels = {
"0": "A", "1": "B", "2": "C", "3": "D", "4": "E", "5": "F", "6": "G", "7": "H", "8": "I", "9": "J",
"10": "K", "11": "L", "12": "M", "13": "N", "14": "O", "15": "P", "16": "Q", "17": "R", "18": "S", "19": "T",
"20": "U", "21": "V", "22": "W", "23": "X", "24": "Y", "25": "Z"
}
@app.get("/")
def read_root():
return {"message": "Welcome to the Sign Language Detection API!"}
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
# Read image
image_bytes = await file.read()
image = Image.open(BytesIO(image_bytes)).convert("RGB")
# Process and predict
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().tolist()
# Build predictions dict
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
top_prediction = max(predictions, key=predictions.get)
return JSONResponse(content={
"predicted_letter": top_prediction,
"confidence": predictions[top_prediction],
"all_predictions": predictions
})
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
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