feat: functional cetacean classifier

src/classifier/classifier_image.py

@@ -109,10 +109,6 @@ def cetacean_show_results_and_review() -> None:
                 selected_class = st.selectbox(f"Species for observation {str(o)}", viewer.WHALE_CLASSES, index=ix)
             
             _observation.set_selected_class(selected_class)
-            #observation['predicted_class'] = selected_class
-            # this logic is now in the InputObservation class automatially
-            #if selected_class != st.session_state.whale_prediction1[hash]:
-            #    observation['class_overriden'] = selected_class # TODO: this should be boolean!
             
             # store the elements of the observation that will be transmitted (not image)
             observation = _observation.to_dict()
@@ -163,27 +159,6 @@ def cetacean_show_results():
     
         with grid[col]:
             st.image(image, use_column_width=True)
-            
-            # # dropdown for selecting/overriding the species prediction
-            # if not st.session_state.classify_whale_done[hash]:
-            #     selected_class = st.sidebar.selectbox("Species", viewer.WHALE_CLASSES, 
-            #                                                     index=None, placeholder="Species not yet identified...", 
-            #                                                     disabled=True)
-            # else:
-            #     pred1 = st.session_state.whale_prediction1[hash]
-            #     # get index of pred1 from WHALE_CLASSES, none if not present
-            #     print(f"[D] pred1: {pred1}")
-            #     ix = viewer.WHALE_CLASSES.index(pred1) if pred1 in viewer.WHALE_CLASSES else None
-            #     selected_class = st.selectbox(f"Species for observation {str(o)}", viewer.WHALE_CLASSES, index=ix)
-            
-            # observation['predicted_class'] = selected_class
-            # if selected_class != st.session_state.whale_prediction1[hash]:
-            #     observation['class_overriden'] = selected_class # TODO: this should be boolean!
-            
-            # st.session_state.public_observation = observation
-            
-            #st.button(f"Upload observation {str(o)} to THE INTERNET!", on_click=push_observations)
-            # 
             st.markdown(metadata2md(hash, debug=True))
 
             msg = f"[D] full observation after inference: {observation}"

src/pages/3_🔥_classifiers.py

@@ -35,8 +35,6 @@ dataset_id = "Saving-Willy/temp_dataset"
 data_files = "data/train-00000-of-00001.parquet"
 ############################################################
 
-st.sidebar.success("classifiers")
-
  # Streamlit app
 tab_inference, tab_hotdogs= \
     st.tabs(["Cetecean classifier", "Hotdog classifier"])
@@ -67,7 +65,7 @@ if st.session_state.workflow_fsm.is_in_state('data_entry_complete'):
     if st.sidebar.button(":white_check_mark:[**Validate**]"):
         # create a dictionary with the submitted observation
 
-        # TO-DO NEED TO ADAPT. 
+        # TODO NEED TO ADAPT to multipage
         #tab_log.info(f"{st.session_state.observations}")
 
         df = pd.DataFrame([obs.to_dict() for obs in st.session_state.observations.values()])
@@ -118,7 +116,9 @@ elif st.session_state.workflow_fsm.is_in_state('ml_classification_completed'):
         st.markdown(s)
 
     # add a button to advance the state
-    if st.button("Confirm species predictions", help="Confirm that all species are selected correctly"):
+    if st.button("I have looked over predictions and confirm correct species", icon= "👀",
+                 type="primary",
+                 help="Confirm that all species are selected correctly"):
         st.session_state.workflow_fsm.complete_current_state()
         # -> manual_inspection_completed
         st.rerun()
@@ -130,7 +130,8 @@ elif st.session_state.workflow_fsm.is_in_state('manual_inspection_completed'):
     st.markdown("""### Inference Results (after manual validation) """)
     
     
-    if st.button("Upload all observations to THE INTERNET!"):
+    if st.button("Upload all observations to THE INTERNET!", icon= "⬆️",
+                 type="primary",):
         # let this go through to the push_all func, since it just reports to log for now.
         push_all_observations(enable_push=False)
         st.session_state.workflow_fsm.complete_current_state()
@@ -146,7 +147,6 @@ elif st.session_state.workflow_fsm.is_in_state('data_uploaded'):
     cetacean_show_results()
 
     st.divider()
-    #df = pd.DataFrame(st.session_state.observations, index=[0])
     df = pd.DataFrame([obs.to_dict() for obs in st.session_state.observations.values()])
     st.table(df)
 

src/utils/metadata_handler.py

@@ -14,7 +14,7 @@ def metadata2md(image_hash:str, debug:bool=False) -> str:
     markdown_str = "\n"
     keys_to_print = ["author_email", "latitude", "longitude", "date", "time"]
     if debug:
-        keys_to_print += ["iamge_md5", "selected_class", "top_prediction", "class_overriden"]
+        keys_to_print += ["image_md5", "selected_class", "top_prediction", "class_overriden"]
 
     observation = st.session_state.public_observations.get(image_hash, {})