fix: states over pages

src/classifier/classifier_image.py

@@ -107,7 +107,6 @@ def cetacean_show_results_and_review() -> None:
                 print(f"[D] {o:3} pred1: {pred1:30} | {hash}")
                 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.set_selected_class(selected_class)
             
             # store the elements of the observation that will be transmitted (not image)
@@ -175,69 +174,3 @@ def cetacean_show_results():
                 viewer.display_whale(whale_classes, i)
         o += 1
         col = (col + 1) % row_size
-
-
-
-
-# func to do all in one
-def cetacean_classify_show_and_review(cetacean_classifier):
-    """Cetacean classifier using the saving-willy model from Saving Willy Hugging Face space.
-    For each image in the session state, classify the image and display the top 3 predictions.
-    Args:
-        cetacean_classifier ([type]):  saving-willy model from Saving Willy Hugging Face space
-    """
-    raise DeprecationWarning("This function is deprecated. Use individual steps instead")
-    images = st.session_state.images
-    observations = st.session_state.observations
-    hashes = st.session_state.image_hashes
-    batch_size, row_size, page = gridder(hashes)
-    
-    grid = st.columns(row_size)
-    col = 0
-    o=1
-    for hash in hashes: 
-        image = images[hash]
-        
-        with grid[col]:
-            st.image(image, use_column_width=True)
-            observation = observations[hash].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[hash] = out['predictions'][0]
-            st.session_state.classify_whale_done[hash] = True
-            msg = f"[D]2 classify_whale_done for {hash}: {st.session_state.classify_whale_done[hash]}, whale_prediction1: {st.session_state.whale_prediction1[hash]}"
-            g_logger.info(msg)
-            
-            # 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
-            
-            st.session_state.public_observation = observation
-            st.button(f"Upload observation {str(o)} to THE INTERNET!", on_click=push_observations)
-            # TODO: the metadata only fills properly if `validate` was clicked.
-            st.markdown(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)
-            st.markdown(f"Top 3 Predictions for observation {str(o)}")
-            for i in range(len(whale_classes)):
-                viewer.display_whale(whale_classes, i)
-        o += 1
-        col = (col + 1) % row_size

src/pages/3_🔥_classifiers.py

@@ -7,7 +7,7 @@ st.set_page_config(
     page_icon="🔥",
 )
 
-from utils.st_logs import parse_log_buffer, init_logging_session_states
+from utils.st_logs import init_logging_session_states
 
 from transformers import pipeline
 from transformers import AutoModelForImageClassification
@@ -25,12 +25,14 @@ from classifier.classifier_hotdog import hotdog_classify
 
 # setup for the ML model on huggingface (our wrapper)
 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+# one toggle for all the extra debug text
+if "MODE_DEV_STATEFUL" not in st.session_state:
+    st.session_state.MODE_DEV_STATEFUL = False
 
 ############################################################
-# TO- DO: MAKE ENV FILE 
+classifier_name = "Saving-Willy/cetacean-classifier"
 #classifier_revision = '0f9c15e2db4d64e7f622ade518854b488d8d35e6'
 classifier_revision = 'main' # default/latest version
-# and the dataset of observations (hf dataset in our space)
 dataset_id = "Saving-Willy/temp_dataset"
 data_files = "data/train-00000-of-00001.parquet"
 ############################################################
@@ -100,7 +102,7 @@ if st.session_state.workflow_fsm.is_in_state('data_entry_validated'):
     if tab_inference.button("Identify with cetacean classifier", 
                             key="button_infer_ceteans"):
         cetacean_classifier = AutoModelForImageClassification.from_pretrained(
-            "Saving-Willy/cetacean-classifier", 
+            classifier_name, 
             revision=classifier_revision, 
             trust_remote_code=True)
 
@@ -161,7 +163,7 @@ elif st.session_state.workflow_fsm.is_in_state('data_uploaded'):
     # didn't decide what the next state is here - I think we are in the terminal state.
     #st.session_state.workflow_fsm.complete_current_state()
         
-    
+  
 # inside the hotdog tab, on button press we call a 2nd model (totally unrelated at present, just for demo
 # purposes, an hotdog image classifier) which will be run locally.
 # - this model predicts if the image is a hotdog or not, and returns probabilities

src/whale_viewer.py

@@ -157,4 +157,6 @@ def display_whale(whale_classes:List[str], i:int, viewcontainer:DeltaGenerator=N
     image_path = os.path.join(current_dir, "src/images/references/")
     image = Image.open(image_path + df_whale_img_ref.loc[whale_classes[i], "WHALE_IMAGES"])
 
-    viewcontainer.image(image, caption=df_whale_img_ref.loc[whale_classes[i], "WHALE_REFERENCES"], use_column_width=True)
+    viewcontainer.image(image, 
+                        # caption=df_whale_img_ref.loc[whale_classes[i], "WHALE_REFERENCES"], 
+                        use_column_width=True)