feat: create knowledge interface

components/inspector.py

@@ -1,5 +1,5 @@
 import streamlit as st
-import pickle
+import knowledge
 from datetime import datetime
 import numpy as np
 import time
@@ -9,15 +9,12 @@ def get_name():
     return '🔍 inspector'
 
 
-@st.cache(persist=True, allow_output_mutation=True)
-def load_thoughts():
-    return pickle.load(open('conceptarium/metadata.pickle', 'rb'))
-
-
 def paint():
     if st.session_state.get('navigator_embedding', None) is not None:
-        thoughts = load_thoughts()
-        match = [e for e in thoughts if e.get_content() == st.session_state['navigator_input']]
+        thoughts = knowledge.load()
+
+        match = [
+            e for e in thoughts if st.session_state['navigator_input'] == e.get_content()]
 
         if len(match) > 0:
             thought = match[0]
@@ -29,13 +26,13 @@ def paint():
 
             st.markdown('**modality**: ' + thought.modality)
             st.markdown('**filename**: ' + thought.filename.split('/')[-1])
-            st.markdown('**timestamp**: ' + datetime.utcfromtimestamp(int(thought.timestamp)).strftime("%d.%m.%Y"))
-            st.markdown('**activation**: ' + str(round(np.log(thought.interest / (1 - 0.9)) - 0.9 * np.log((time.time() - thought.timestamp) / (3600 * 24) + 0.1), 2)))
+            st.markdown('**timestamp**: ' + datetime.utcfromtimestamp(
+                int(thought.timestamp)).strftime("%d.%m.%Y"))
+            st.markdown('**activation**: ' + str(round(np.log(thought.interest / (1 - 0.9)) -
+                        0.9 * np.log((time.time() - thought.timestamp) / (3600 * 24) + 0.1), 2)))
         else:
             st.markdown('**type**: custom query')
             if st.session_state['navigator_modality'] == 'language':
                 st.success(st.session_state['navigator_input'])
             elif st.session_state['navigator_modality'] == 'imagery':
                 st.image(st.session_state['navigator_input'])
-
-    

components/navigator.py

@@ -2,8 +2,8 @@ import streamlit as st
 from sentence_transformers import SentenceTransformer
 import io
 from PIL import Image
-import pickle
 import torch
+import knowledge
 
 
 def get_name():
@@ -17,17 +17,19 @@ def load_model():
 
 @st.cache(persist=True, allow_output_mutation=True)
 def load_imagery_shift():
-    thoughts = pickle.load(open('conceptarium/metadata.pickle', 'rb'))
-    langauge_centroid = torch.mean(torch.stack([e.embedding for e in thoughts if e.modality == 'language']), -2)
-    imagery_centroid = torch.mean(torch.stack([e.embedding for e in thoughts if e.modality == 'imagery']), -2)
-    
+    thoughts = knowledge.load()
+    langauge_centroid = torch.mean(torch.stack(
+        [e.embedding for e in thoughts if e.modality == 'language']), -2)
+    imagery_centroid = torch.mean(torch.stack(
+        [e.embedding for e in thoughts if e.modality == 'imagery']), -2)
+
     return langauge_centroid - imagery_centroid
 
 
 def paint():
     model = load_model()
     modality = st.selectbox('modality', ['language', 'imagery'],
-        ['language', 'imagery'].index(st.session_state.get('navigator_modality', 'language')))
+                            ['language', 'imagery'].index(st.session_state.get('navigator_modality', 'language')))
     embedding = None
 
     if modality == 'language':
@@ -37,7 +39,8 @@ def paint():
             value = ''
 
         input = st.text_area('input', value=value, height=300)
-        embedding = model.encode(input, convert_to_tensor=True, normalize_embeddings=True)
+        embedding = model.encode(
+            input, convert_to_tensor=True, normalize_embeddings=True)
     elif modality == 'imagery':
         if st.session_state.get('navigator_modality', None) == 'imagery':
             value = st.session_state['navigator_input']
@@ -45,9 +48,10 @@ def paint():
             value = None
 
         input = st.file_uploader('input')
-        
+
         if input is not None:
-            embedding = model.encode(Image.open(io.BytesIO(input.getvalue())), convert_to_tensor=True, normalize_embeddings=True) + load_imagery_shift()
+            embedding = model.encode(Image.open(io.BytesIO(input.getvalue(
+            ))), convert_to_tensor=True, normalize_embeddings=True) + load_imagery_shift()
         elif value is not None:
             embedding = st.session_state['navigator_embedding']
 
@@ -55,5 +59,3 @@ def paint():
         st.session_state['navigator_modality'] = modality
         st.session_state['navigator_embedding'] = embedding
         st.session_state['navigator_input'] = input
-        
-    

components/ranker.py

@@ -6,15 +6,9 @@ def get_name():
 
 
 def paint():
-    st.session_state['ranker_relatedness'] = st.slider('relatedness', -1., 1., 0.8, 0.01, help='the weight of semantic relatedness to the query')
-    st.session_state['ranker_activation'] = st.slider('activation', -1., 1., 0., 0.01, help='the weight of thought activation')
-    st.session_state['ranker_noise'] = st.slider('noise', 0., 0.1, 0.01, 0.001, help='the amount of noise added')
-
-    # relatedness = st.slider('relatedness', -1., 1., 0.8, 0.01, help='the weight of semantic relatedness to the query')
-    # activation = st.slider('activation', -1., 1., 0., 0.01, help='the weight of thought activation')
-    # st.session_state['ranker_noise'] = st.slider('noise', 0., 0.1, 0.01, 0.001, help='the amount of noise added')
-
-    # if st.button('set'):
-    #     st.session_state['ranker_relatedness'] = relatedness
-    #     st.session_state['ranker_activation'] = activation
-    #     st.session_state['ranker_noise'] = noise
+    st.session_state['ranker_relatedness'] = st.slider(
+        'relatedness', -1., 1., 0.8, 0.01, help='the weight of semantic relatedness to the query')
+    st.session_state['ranker_activation'] = st.slider(
+        'activation', -1., 1., 0., 0.01, help='the weight of thought activation')
+    st.session_state['ranker_noise'] = st.slider(
+        'noise', 0., 0.1, 0.01, 0.001, help='the amount of noise added')

components/viewport.py

@@ -1,5 +1,5 @@
 import streamlit as st
-import pickle
+import knowledge
 from sentence_transformers import util
 import torch
 import numpy as np
@@ -10,21 +10,23 @@ def get_name():
     return '🪟 viewport'
 
 
-@st.cache(persist=True, allow_output_mutation=True)
 def load_thoughts():
-    thoughts = pickle.load(open('conceptarium/metadata.pickle', 'rb'))
-    langauge_centroid = torch.mean(torch.stack([e.embedding for e in thoughts if e.modality == 'language']), -2)
-    imagery_centroid = torch.mean(torch.stack([e.embedding for e in thoughts if e.modality == 'imagery']), -2)
-    
+    thoughts = knowledge.load()
+    langauge_centroid = torch.mean(torch.stack(
+        [e.embedding for e in thoughts if e.modality == 'language']), -2)
+    imagery_centroid = torch.mean(torch.stack(
+        [e.embedding for e in thoughts if e.modality == 'imagery']), -2)
+
     print(imagery_centroid - langauge_centroid)
 
     for thought_idx, thought in enumerate(thoughts):
         if thought.modality == 'imagery':
-            thoughts[thought_idx].embedding += langauge_centroid - imagery_centroid
-    
+            thoughts[thought_idx].embedding += langauge_centroid - \
+                imagery_centroid
+
     return thoughts
 
-    
+
 def paint(cols):
     if st.session_state.get('navigator_embedding', None) is not None:
         thoughts = load_thoughts()
@@ -35,11 +37,12 @@ def paint(cols):
 
         for result_idx, result in enumerate(results):
             results[result_idx]['score'] = (st.session_state['ranker_relatedness'] * result['score']
-                                    + st.session_state['ranker_activation'] *
-                                    (np.log(thoughts[result['corpus_id']].interest / (1 - 0.9)) - 0.9 * np.log((time.time() - thoughts[result['corpus_id']].timestamp) / (3600 * 24) + 0.1))) \
+                                            + st.session_state['ranker_activation'] *
+                                            (np.log(thoughts[result['corpus_id']].interest / (1 - 0.9)) - 0.9 * np.log((time.time() - thoughts[result['corpus_id']].timestamp) / (3600 * 24) + 0.1))) \
                 * np.random.normal(1, st.session_state['ranker_noise'])
 
-        results = sorted(results, key=lambda result: result['score'], reverse=True)
+        results = sorted(
+            results, key=lambda result: result['score'], reverse=True)
 
         if thoughts[results[0]['corpus_id']].get_content() == st.session_state['navigator_input']:
             results = results[1:]
@@ -57,5 +60,3 @@ def paint(cols):
                     st.session_state['navigator_modality'] = thought.modality
                     st.session_state['navigator_embedding'] = thought.embedding
                     st.experimental_rerun()
-
-    

knowledge.py

@@ -0,0 +1,10 @@
+import streamlit as st
+import pickle
+
+
+def load():
+    if st.session_state['authentication_status']:
+        thoughts = pickle.load(open('conceptarium/metadata.pickle', 'rb'))
+        return thoughts
+    else:
+        return []