fix: bug in playground

evonote/gui/dictionary_viewer.py

@@ -6,7 +6,7 @@
 
 def show_document_with_key_gui(keys, documents):
     if len(keys) == 0:
-        print("No keys to show")
+        print("Dict viewer: No keys to show")
         return
 
     html_documents = []

evonote/mindtree/tree.py

@@ -24,7 +24,7 @@ class Tree:
     """
 
 
-    def __init__(self, root_content, rule_of_path: str = None):
+    def __init__(self, root_content="", rule_of_path: str = None):
         """
         :param root_content: The content of the root of the tree
         :param rule_of_path: The rule for creating paths.

evonote/transform/build_from_sections.py

@@ -60,7 +60,7 @@ def digest_content(content):
         system_message="""You are a helpful assistant for arranging knowledge. You should output merely JSON.""")
     chat.add_user_message(content)
     chat.add_user_message(
-        """Summarize the below paragraphs into a tree. Give the result in JSON with the keys being "root_content", "statement", "subtopics". The "statement" entry should be a shortened version of original text.""")
+        """Summarize the below paragraphs into a tree. Give the result in JSON with the keys being "topic", "statement", "subtopics". The "statement" entry should be a shortened version of original text.""")
 
     res = chat.complete_chat()
     if res[0] == "`":
@@ -83,9 +83,9 @@ def set_notes_by_digest(note: Note, digest: str):
 
 
 def iter_and_assign(note: Note, tree: dict):
-    if "root_content" not in tree or "statement" not in tree:
+    if "topic" not in tree or "statement" not in tree:
         raise Exception("incomplete tree node")
-    node = note.s(tree["root_content"]).be(tree["statement"])
+    node = note.s(tree["topic"]).be(tree["statement"])
     if "subtopics" not in tree:
         return
     for subtopic in tree["subtopics"]:

playground/gen_sample_paper_tree.py

@@ -0,0 +1,17 @@
+from evonote.file_helper.cache_manage import save_used_cache
+from evonote.testing.sample_paper import sample_paper
+from evonote.transform.build_from_sections import digest_all_descendants, \
+    mindtree_from_doc
+from evonote.data_cleaning.latex_converter import process_latex_into_standard
+
+tex = sample_paper
+
+doc, meta = process_latex_into_standard(tex)
+
+paper_tree = mindtree_from_doc(doc, meta)
+
+digest_tree = digest_all_descendants(paper_tree)
+
+digest_tree.save("AI4Science.enb")
+
+save_used_cache()

playground/persistence.py

@@ -1,5 +1,8 @@
+import os
+
 from evonote.mindtree import Tree
 
+os.system("python gen_sample_paper_tree.py")
 tree = Tree.load("AI4Science.enb")
 
 tree.show_tree_gui()

playground/search_in_mindtree.py

@@ -1,5 +1,8 @@
+import os
+
 from evonote.mindtree import Tree
 
+os.system("python gen_sample_paper_tree.py")
 tree = Tree.load("AI4Science.enb")
 
 keyword = "quantum computing"

playground/notebook_to_paragraph.py → playground/tree_to_paragraph.py

@@ -1,19 +1,20 @@
+import os
+
 from evonote import debug
 from evonote.transform.build_from_sections import digest_all_descendants
 from evonote.transform.tree_to_paragraph import tree_to_paragraph
 from evonote.mindtree import Tree
 
 
 with debug.display_chats():
+    os.system("python gen_sample_paper_tree.py")
     tree = Tree.load("AI4Science.enb")
     keyword = "quantum computing"
     tree = tree.get_sub_tree_by_similarity([keyword], top_k=6)
     res = tree_to_paragraph(tree,
                                 f"You should focus on writing about {keyword}")
-
-    # Refactor the tree
-
-    new_tree = Tree(keyword)
+    print(res)
+    new_tree = Tree()
     note = new_tree.get_new_note_by_path([keyword])
     note.be(res)
     new_tree = digest_all_descendants(new_tree)

playground/visualize_paper.py

@@ -1,26 +1,7 @@
-from evonote.file_helper.cache_manage import save_used_cache, save_cache
-from evonote.testing.sample_paper import sample_paper
-from evonote.transform.build_from_sections import digest_all_descendants, \
-    mindtree_from_doc
-from evonote.data_cleaning.latex_converter import process_latex_into_standard
+import os
 
+from evonote.mindtree import Tree
 
-tex = sample_paper
-
-doc, meta = process_latex_into_standard(tex)
-
-paper_tree = mindtree_from_doc(doc, meta)
-
-digest_tree = digest_all_descendants(paper_tree)
-
-# Try removing comments to show the tree before digesting
-
-# paper_tree.show_tree_gui()
-
-digest_tree.show_tree_gui()
-
-save_used_cache()
-
-# Try removing comments to save the tree
-
-digest_tree.save("AI4Science.enb")
+os.system("python gen_sample_paper_tree.py")
+tree = Tree.load("AI4Science.enb")
+tree.show_tree_gui()