Dumitrana-Mihnea
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
from fastapi import FastAPI, Request
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import uvicorn
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from starlette.middleware.cors import CORSMiddleware
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import pickle
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import numpy as np
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app = FastAPI()
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origins = ["http://localhost"]
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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model_dict = pickle.load(open("./model.p", "rb"))
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model = model_dict["model"]
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labels_dict = {0: "hello", 1: "i love you", 2: "yes", 3: "good", 4: "bad", 5: "okay", 6: "you", 7: "i/i'm", 8: "why", 9: "no"}
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@app.get("/")
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async def read_root():
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return {"message": "Hello World"}
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@app.post("/")
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async def process_json(request: Request):
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# Get the JSON data from the request
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data = await request.json()
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if len(data) == 0:
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return {"result": "no data"}
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hands_data = data
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data_aux = []
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# hand landmarks are taken from data.json
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if len(hands_data) == 42:
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hand = hands_data[:21]
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else:
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hand = hands_data
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for landmark in hand:
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x = landmark["x"]
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y = landmark["y"]
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data_aux.append(x)
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data_aux.append(y)
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prediction = model.predict([np.asarray(data_aux)])
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predicted_sign = labels_dict[int(prediction[0])]
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print(predicted_sign)
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return {"result": predicted_sign}
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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