change conversion

app.py

@@ -26,10 +26,11 @@ def raw_preds_to_df(raw, quantiles = None):
     in the output, time_idx is the first prediction time index (one step after knowledge cutoff)
     pred_idx the index of the predicted date i.e. time_idx + h - 1
     """
-    index = raw.index
-    preds = raw.output.prediction
-    dec_len = raw.output.prediction.shape[1]
-    n_quantiles = preds.shape[-1]
+    index = raw[2]
+    output = raw[0]
+    preds = output.prediction
+    dec_len = output.prediction.shape[1]
+    n_quantiles = output.prediction.shape[-1]
     preds_df = pd.DataFrame(index.values.repeat(dec_len * n_quantiles, axis=0),columns=index.columns)
     preds_df = preds_df.assign(h=np.tile(np.repeat(np.arange(1,1+dec_len),n_quantiles),len(preds_df)//(dec_len*n_quantiles)))
     preds_df = preds_df.assign(q=np.tile(np.arange(n_quantiles),len(preds_df)//n_quantiles))
@@ -39,7 +40,7 @@ def raw_preds_to_df(raw, quantiles = None):
 
     preds_df['pred_idx'] = preds_df['time_idx'] + preds_df['h'] - 1
     return preds_df
-
+    
 def prepare_dataset(_parameters, df, rain, temperature, datepicker, mapping):
     if rain != "Default":
         df["MTXWTH_Day_precip"] = mapping[rain]
@@ -96,7 +97,7 @@ def generate_plot(df):
 
 @st.cache_data
 def load_data():
-    with open('data/parameters.pkl', 'rb') as f:
+    with open('data/parameters_q.pkl', 'rb') as f:
         parameters = pickle.load(f)
     df = pd.read_pickle('data/test_data.pkl')
     df = df.loc[(df["Branch"] == "15") & (df["Group"].isin(["6","7","4","1"]))]
@@ -104,7 +105,7 @@ def load_data():
 
 @st.cache_resource
 def init_model():
-    model = TemporalFusionTransformer.load_from_checkpoint('model/tft_check.ckpt', map_location=torch.device('cpu'))
+    model = TemporalFusionTransformer.load_from_checkpoint('model/tft_check_q.ckpt', map_location=torch.device('cpu'))
     return model
     
 def main():