app = Flask(__name__)
@app.route("/", methods=['POST','GET'])
def index():
if (request.method == "POST"):
data = request.form['content']
# run()
try:
numbers = list(map(lambda x: float(x), data.split(',')))
output = single_predict(model, numbers)
except:
output = "You have to write 4 numbers separated by commas"
return render_template('index.html', value = output)
else:
return render_template('index.html')
if __name__ == '__main__':
model = Model()
model.load_state_dict(torch.load('models/model_v1'))
app.run(debug=True)
def predict_basin(
basin: str,
run_dir: Union[str, Path],
camels_dir: Union[str, Path],
period: str = "train",
epoch: int = 30,
):
if isinstance(run_dir, str):
run_dir = Path(run_dir)
elif not isinstance(run_dir, Path):
raise TypeError(f"run_dir must be str or Path, not {type(run_dir)}")
if isinstance(camels_dir, str):
camels_dir = Path(camels_dir)
elif not isinstance(camels_dir, Path):
raise TypeError(f"run_dir must be str or Path, not {type(camels_dir)}")
with open(run_dir / "cfg.json", "r") as fp:
run_cfg = json.load(fp)
if not period in ["train", "val"]:
raise ValueError("period must be either train or val")
basins = get_basin_list()
db_path = str(run_dir / "attributes.db")
attributes = load_attributes(db_path=db_path,
basins=basins,
drop_lat_lon=True)
means = attributes.mean()
stds = attributes.std()
attrs_count = len(attributes.columns)
timeseries_count = 6
input_size_stat = timeseries_count if run_cfg["no_static"] else attrs_count
input_size_dyn = (timeseries_count if
(run_cfg["no_static"] or not run_cfg["concat_static"])
else timeseries_count + attrs_count)
model = Model(
input_size_dyn=input_size_dyn,
input_size_stat=input_size_stat,
hidden_size=run_cfg["hidden_size"],
dropout=run_cfg["dropout"],
concat_static=run_cfg["concat_static"],
no_static=run_cfg["no_static"],
).to(DEVICE)
# load trained model
weight_file = run_dir / f"model_epoch{epoch}.pt"
model.load_state_dict(torch.load(weight_file, map_location=DEVICE))
ds_test = CamelsTXT(
camels_root=camels_dir,
basin=basin,
dates=[
GLOBAL_SETTINGS[f"{period}_start"],
GLOBAL_SETTINGS[f"{period}_end"]
],
is_train=False,
seq_length=run_cfg["seq_length"],
with_attributes=True,
attribute_means=means,
attribute_stds=stds,
concat_static=run_cfg["concat_static"],
db_path=db_path,
)
date_range = ds_test.dates_index[run_cfg["seq_length"] - 1:]
loader = DataLoader(ds_test, batch_size=1024, shuffle=False, num_workers=4)
preds, obs = evaluate_basin(model, loader)
df = pd.DataFrame(data={
"qobs": obs.flatten(),
"qsim": preds.flatten()
},
index=date_range)
results = df
# plt.plot(date_range, results["qobs"], label="Obs")
# plt.plot(date_range, results["qsim"], label="Preds")
# plt.legend()
# plt.savefig(f"{run_dir}/pred_basin_{basin}.pdf")
# plt.close()
return results, date_range
