cleanup: update app.py

app.py

@@ -175,7 +175,7 @@ def compute_convective_core_masks(ir_data):
 # ------------------ Streamlit UI ------------------
 st.set_page_config(page_title="TCIR Daily Input", layout="wide")
 
-st.title("Tropical Cyclone Input Uploader (8 sets/day)")
+st.title("Tropical Cyclone U-Net Wind Speed (Intensity) Predictor")
 
 ir_images = st.file_uploader("Upload 8 IR images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
 pmw_images = st.file_uploader("Upload 8 PMW images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
@@ -188,16 +188,6 @@ else:
 st.header("Input Latitude, Longitude, Vmax")
 lat_values, lon_values, vmax_values = [], [], []
 
-# col1, col2, col3 = st.columns(3)
-# with col1:
-#     for i in range(8):
-#         lat_values.append(st.number_input(f"Latitude {i+1}", key=f"lat{i}"))
-# with col2:
-#     for i in range(8):
-#         lon_values.append(st.number_input(f"Longitude {i+1}", key=f"lon{i}"))
-# with col3:
-#     for i in range(8):
-#         vmax_values.append(st.number_input(f"Vmax {i+1}", key=f"vmax{i}"))
 import pandas as pd
 
 import numpy as np
@@ -220,7 +210,6 @@ if csv_file is not None:
             vmax_values = np.array(vmax_values)
 
             st.success("CSV file loaded and processed successfully!")
-            # Optional: Show dataframe or data shape
             st.write(df.head())
 
         else:
@@ -258,24 +247,18 @@ if st.button("Submit for Processing"):
         elif model_choice == "spatiotemporalLSTM":
             from spaio_temp import predict_stlstm
             model_predict_fn = predict_stlstm
-        # from gru_model import predict
-        # from convlstm import predict_lstm
-        # from cnn3d import predict_3dcnn
-        # from trjgru import predict_trajgru
-        # from spaio_temp import predict_stlstm
-        # from unetlstm import predict_unetlstm
+
         ir_arrays = []
         pmw_arrays = []
         train_vmax_2d = reshape_vmax(np.array(vmax_values))
-        # st.write("Vmax 2D shape:", train_vmax_2d.shape)
+
         train_vmax_3d= create_3d_vmax(train_vmax_2d)
-        # st.write("Vmax 3D shape:", train_vmax_3d.shape)
+
         lat_processed = process_lat_values(lat_values)
         lon_processed = process_lon_values(lon_values)
-        # st.write("Lat 2D shape:", lat_processed.shape)
-        # st.write("Lon 2D shape:", lon_processed.shape)
+
         v_max_diff = calculate_intensity_difference(train_vmax_2d)
-        # st.write("Vmax Intensity Difference shape:", v_max_diff.shape)
+
         for ir in ir_images:
             img = Image.open(ir).convert("L")
             arr = np.array(img).astype(np.float32)
@@ -290,50 +273,26 @@ if st.button("Submit for Processing"):
             pmw_arrays.append(resized)
         ir=np.array(ir_arrays)
         pmw=np.array(pmw_arrays)
+        
         # Stack into (8, 95, 95)
         ir_seq = process_images(ir)
         pmw_seq = process_images(pmw)
 
-        # st.write(f"IR sequence shape: {ir_seq.shape}")
-        # st.write(f"PMW sequence shape: {pmw_seq.shape}")
 
         # For demonstration: create batches
         X_train_new = ir_seq.reshape((1, 8, 95, 95)) # Shape: (1, 8, 95, 95)
-        # X_test_new = X_valid = X_train_new.copy()     # Dummy copies for now
-        # st.write(f"X_train_new shape: {X_train_new.shape}")
+  
         cc_mask= compute_convective_core_masks(X_train_new)
-        # st.write("CC Mask Shape:", cc_mask.shape)
         hov_m_train = generate_hovmoller(X_train_new)
-        # hov_m_test = generate_hovmoller(X_test_new)
-        # hov_m_valid = generate_hovmoller(X_valid)
         hov_m_train[np.isnan(hov_m_train)] = 0 
         hov_m_train = hov_m_train.transpose(0, 2, 3, 1) 
-        # st.success("Hovmöller diagrams generated ✅")
-        # st.write("Hovmöller Train Shape:", hov_m_train.shape)
-        # st.write("Hovmöller Test Shape:", hov_m_test.shape)
-        # st.write("Hovmöller Valid Shape:", hov_m_valid.shape)
-        
-        # Visualize first sample
-        # st.subheader("Hovmöller Sample (Train Set)")
-        # for t in range(8):
-        #     st.image(hov_m_train[0, t], caption=f"Time Step {t+1}", clamp=True, width=150)
-        # st.write(hov_m_train[0,0])
+
         cc_mask[np.isnan(cc_mask)] = 0
         cc_mask=cc_mask.reshape(1, 8, 95, 95, 1)
         i_images=cc_mask+ir_seq
         reduced_images = np.concatenate([i_images,pmw_seq ], axis=-1)
         reduced_images[np.isnan(reduced_images)] = 0
-        # st.write("Reduced Images Shape:", reduced_images.shape)
-        # y=np.isnan(reduced_images).sum()
-        # st.write("Reduced Images NaN Count:", y)
-        # model_predict_fn = {
-        #     "ConvGRU": predict,
-        #     "ConvLSTM": predict_lstm,
-        #     "3DCNN":predict_3dcnn,
-        #     "Traj-GRU": predict_trajgru,
-        #     "spatiotemporalLSTM": predict_stlstm,
-        #     "Unet_LSTM": predict_unetlstm,
-        # }[model_choice]
+
         if model_choice == "Unet_LSTM":
             import tensorflow as tf