chore: removing duplicate file (right one is in classifier subdir)

src/classifier_image.py

@@ -1,70 +0,0 @@
-import streamlit as st
-import logging
-import os
-
-# get a global var for logger accessor in this module
-LOG_LEVEL = logging.DEBUG
-g_logger = logging.getLogger(__name__)
-g_logger.setLevel(LOG_LEVEL)
-
-from grid_maker import gridder
-import hf_push_observations as sw_push_obs
-import utils.metadata_handler as meta_handler
-import whale_viewer as sw_wv
-
-def cetacean_classify(cetacean_classifier, tab_inference):
-    files = st.session_state.files
-    images = st.session_state.images
-    observations = st.session_state.observations
-
-    batch_size, row_size, page = gridder(files)
-    
-    grid = st.columns(row_size)
-    col = 0
-
-    for file in files: 
-        image = images[file.name]
-        
-        with grid[col]:
-            st.image(image, use_column_width=True)
-            observation = observations[file.name].to_dict()
-            # run classifier model on `image`, and persistently store the output
-            out = cetacean_classifier(image) # get top 3 matches
-            st.session_state.whale_prediction1 = out['predictions'][0]
-            st.session_state.classify_whale_done = True
-            msg = f"[D]2 classify_whale_done: {st.session_state.classify_whale_done}, whale_prediction1: {st.session_state.whale_prediction1}"
-            g_logger.info(msg)
-            
-            # dropdown for selecting/overriding the species prediction
-            if not st.session_state.classify_whale_done:
-                selected_class = st.sidebar.selectbox("Species", sw_wv.WHALE_CLASSES, 
-                                                                index=None, placeholder="Species not yet identified...", 
-                                                                disabled=True)
-            else:
-                pred1 = st.session_state.whale_prediction1
-                # get index of pred1 from WHALE_CLASSES, none if not present
-                print(f"[D] pred1: {pred1}")
-                ix = sw_wv.WHALE_CLASSES.index(pred1) if pred1 in sw_wv.WHALE_CLASSES else None
-                selected_class = tab_inference.selectbox("Species", sw_wv.WHALE_CLASSES, index=ix)
-            
-            observation['predicted_class'] = selected_class
-            if selected_class != st.session_state.whale_prediction1:
-                observation['class_overriden'] = selected_class
-            
-            st.session_state.public_observation = observation
-            st.button(f"Upload observation for {file.name} to THE INTERNET!", on_click=sw_push_obs.push_observations)
-            # TODO: the metadata only fills properly if `validate` was clicked.
-            st.markdown(meta_handler.metadata2md())
-
-            msg = f"[D] full observation after inference: {observation}"
-            g_logger.debug(msg)
-            print(msg)
-            # TODO: add a link to more info on the model, next to the button.
-
-            whale_classes = out['predictions'][:]
-            # render images for the top 3 (that is what the model api returns)
-            #with tab_inference:
-            st.title(f"Species detected for {file.name}")
-            for i in range(len(whale_classes)):
-                sw_wv.display_whale(whale_classes, i)
-        col = (col + 1) % row_size