Modified post request

main.py

@@ -38,7 +38,7 @@ app.mount("/", StaticFiles(directory="static", html=True), name="static")
 def home() -> FileResponse:
 	return FileResponse(path="/app/static/index.html", media_type="text/html")
 
-@app.get("/predict")
+@app.post("/predict")
 def predict_UniKP_values(sequence: str = Form(...), smiles: str = Form(...)) -> Dict[str, float]:
 	
 	endpointHandler = EndpointHandler()
@@ -69,7 +69,8 @@ class EndpointHandler():
 		# load the vocab and trfm model
 		self.vocab = WordVocab(vocab_content)
 		self.trfm = TrfmSeq2seq(len(self.vocab), 256, len(self.vocab), 4)
-		self.trfm.load_state_dict(torch.load(trfm_path))
+		device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+		self.trfm.load_state_dict(torch.load(trfm_path), map_location=device)
 		self.trfm.eval()
 
 		# path to the pretrained models

static/script.js

@@ -15,7 +15,15 @@ document.querySelector("#predict-form").addEventListener("submit", async (e) =>
 });
 
 const prediction = async (sequence, smiles) => {
-	const response = await fetch(`predict?sequence=${sequence}&smiles=${smiles}`);
+	// send POST request with FormData
+	const formData = new FormData();
+	formData.append("sequence", sequence);
+	formData.append("smiles", smiles);
+
+	const response = await fetch("/predict", {
+		method: "POST",
+		body: formData,
+	});
 	const data = await response.json();
 	return data;
 };