app.py

from fastapi import FastAPI, Request
from tensorflow.keras.models import load_model
import numpy as np

app = FastAPI()

# Load model and labels
model = load_model("lstm_model.h5")
actions = [
    "hello", "fine", "goodbye", "name", "friend", "one", "sick", "help", "deaf", "hearing",
    "what", "from", "email", "weather", "rain", "three", "tuesday", "april", "child", "old",
    "9oclock", "5dollars", "8hours", "soon", "lastweek"
]

@app.post("/predict")
async def predict(request: Request):
    data = await request.json()

    try:
        input_data = np.array(data["input"])
    except (KeyError, TypeError, ValueError):
        return {"error": "Invalid input. Expecting JSON with key 'input' and a list of numbers."}

    # Expand dims if the model expects (1, timesteps, features)
    try:
        prediction = model.predict(np.expand_dims(input_data, axis=0))
        predicted_class_idx = int(np.argmax(prediction[0]))
        predicted_label = actions[predicted_class_idx]
    except Exception as e:
        return {"error": str(e)}

    return {
        "class_index": predicted_class_idx,
        "label": predicted_label,
        "confidence": float(np.max(prediction[0]))
    }
    
@app.get("/")
async def root():
    return {"message": "Your ASL LSTM model API is up and running!"}