uploaded site and api data

api and aux scripts/main.py

@@ -0,0 +1,53 @@
+from fastapi import FastAPI, Request
+import uvicorn
+from starlette.middleware.cors import CORSMiddleware
+import pickle
+import numpy as np
+
+app = FastAPI()
+
+origins = ["http://localhost"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_dict = pickle.load(open("./model.p", "rb"))
+model = model_dict["model"]
+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"}
+
+@app.get("/")
+async def read_root():
+    return {"message": "Hello World"}
+
+@app.post("/")
+async def process_json(request: Request):
+    # Get the JSON data from the request
+    data = await request.json()
+    if len(data) == 0:
+        return {"result": "no data"}
+    hands_data = data
+    data_aux = []
+
+    # hand landmarks are taken from data.json
+    if len(hands_data) == 42:
+        hand = hands_data[:21]
+    else:
+        hand = hands_data
+
+    for landmark in hand:
+        x = landmark["x"]
+        y = landmark["y"]
+        data_aux.append(x)
+        data_aux.append(y)
+
+    prediction = model.predict([np.asarray(data_aux)])
+    predicted_sign = labels_dict[int(prediction[0])]
+    print(predicted_sign)
+    return {"result": predicted_sign}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)