Merge pull request #10 from debanjum/saba/configui

Create a UI from which a user can update their config and integrate BaseModel for the config input

.gitignore

@@ -3,4 +3,6 @@ __pycache__
 .emacs.desktop*
 tests/data/.*
 src/.data
-.vscode
+.vscode
+*.gz
+*.pt

environment.yml

@@ -13,4 +13,6 @@ dependencies:
   - pytest=6.*
   - pillow=8.*
   - torchvision=0.*
-  - openai=0.*
+  - openai=0.*
+  - pydantic=1.*
+  - jinja2=3.0.*

src/main.py

@@ -1,26 +1,49 @@
 # Standard Packages
-import sys
-import json
+import sys, json, yaml
 from typing import Optional
 
 # External Packages
 import uvicorn
-from fastapi import FastAPI
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
 
 # Internal Packages
 from src.search_type import asymmetric, symmetric_ledger, image_search
 from src.utils.helpers import get_absolute_path
 from src.utils.cli import cli
-from src.utils.config import SearchType, SearchModels, TextSearchConfig, ImageSearchConfig, SearchConfig, ProcessorConfig, ConversationProcessorConfig
+from src.utils.config import SearchType, SearchModels, ProcessorConfigModel, ConversationProcessorConfigModel
+from src.utils.rawconfig import FullConfig
 from src.processor.conversation.gpt import converse, message_to_log, message_to_prompt, understand
 
-
 # Application Global State
 model = SearchModels()
-search_config = SearchConfig()
-processor_config = ProcessorConfig()
+processor_config = ProcessorConfigModel()
+config = {}
+config_file = ""
+verbose = 0
 app = FastAPI()
 
+app.mount("/views", StaticFiles(directory="views"), name="views")
+templates = Jinja2Templates(directory="views/")
+
+@app.get('/ui', response_class=HTMLResponse)
+def ui(request: Request):
+    return templates.TemplateResponse("config.html", context={'request': request})
+
+@app.get('/config', response_model=FullConfig)
+def config():
+    return config
+
+@app.post('/config')
+async def config(updated_config: FullConfig):
+    global config
+    config = updated_config
+    with open(config_file, 'w') as outfile:
+        yaml.dump(yaml.safe_load(config.json(by_alias=True)), outfile)
+        outfile.close()
+    return config
 
 @app.get('/search')
 def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
@@ -60,7 +83,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
         return image_search.collate_results(
             hits,
             model.image_search.image_names,
-            search_config.image.input_directory,
+            config.content_type.image.input_directory,
             results_count)
 
     else:
@@ -69,22 +92,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
 
 @app.get('/regenerate')
 def regenerate(t: Optional[SearchType] = None):
-    if (t == SearchType.Notes or t == None) and search_config.notes:
-        # Extract Entries, Generate Embeddings
-        model.notes_search = asymmetric.setup(search_config.notes, regenerate=True)
-
-    if (t == SearchType.Music or t == None) and search_config.music:
-        # Extract Entries, Generate Song Embeddings
-        model.music_search = asymmetric.setup(search_config.music, regenerate=True)
-
-    if (t == SearchType.Ledger or t == None) and search_config.ledger:
-        # Extract Entries, Generate Embeddings
-        model.ledger_search = symmetric_ledger.setup(search_config.ledger, regenerate=True)
-
-    if (t == SearchType.Image or t == None) and search_config.image:
-        # Extract Images, Generate Embeddings
-        model.image_search = image_search.setup(search_config.image, regenerate=True)
-
+    initialize_search(regenerate=True, t=t)
     return {'status': 'ok', 'message': 'regeneration completed'}
 
 
@@ -105,37 +113,40 @@ def chat(q: str):
     return {'status': 'ok', 'response': gpt_response}
 
 
-def initialize_search(config, regenerate, verbose):
+def initialize_search(config: FullConfig, regenerate: bool, t: SearchType = None):
     model = SearchModels()
-    search_config = SearchConfig()
 
     # Initialize Org Notes Search
-    search_config.notes = TextSearchConfig.create_from_dictionary(config, ('content-type', 'org'), verbose)
-    if search_config.notes:
-        model.notes_search = asymmetric.setup(search_config.notes, regenerate=regenerate)
+    if (t == SearchType.Notes or t == None) and config.content_type.org:
+        # Extract Entries, Generate Notes Embeddings
+        model.notes_search = asymmetric.setup(config.content_type.org, regenerate=regenerate, verbose=verbose)
 
     # Initialize Org Music Search
-    search_config.music = TextSearchConfig.create_from_dictionary(config, ('content-type', 'music'), verbose)
-    if search_config.music:
-        model.music_search = asymmetric.setup(search_config.music, regenerate=regenerate)
+    if (t == SearchType.Music or t == None) and config.content_type.music:
+        # Extract Entries, Generate Music Embeddings
+        model.music_search = asymmetric.setup(config.content_type.music, regenerate=regenerate, verbose=verbose)
 
     # Initialize Ledger Search
-    search_config.ledger = TextSearchConfig.create_from_dictionary(config, ('content-type', 'ledger'), verbose)
-    if search_config.ledger:
-        model.ledger_search = symmetric_ledger.setup(search_config.ledger, regenerate=regenerate)
+    if (t == SearchType.Ledger or t == None) and config.content_type.ledger:
+        # Extract Entries, Generate Ledger Embeddings
+        model.ledger_search = symmetric_ledger.setup(config.content_type.ledger, regenerate=regenerate, verbose=verbose)
 
     # Initialize Image Search
-    search_config.image = ImageSearchConfig.create_from_dictionary(config, ('content-type', 'image'), verbose)
-    if search_config.image:
-        model.image_search = image_search.setup(search_config.image, regenerate=regenerate)
+    if (t == SearchType.Image or t == None) and config.content_type.image:
+        # Extract Entries, Generate Image Embeddings
+        model.image_search = image_search.setup(config.content_type.image, regenerate=regenerate, verbose=verbose)
 
-    return model, search_config
+    return model
 
 
-def initialize_processor(config, verbose):
+def initialize_processor(config: FullConfig):
+    if not config.processor:
+        return
+    
+    processor_config = ProcessorConfigModel()
+
     # Initialize Conversation Processor
-    processor_config = ProcessorConfig()
-    processor_config.conversation = ConversationProcessorConfig.create_from_dictionary(config, ('processor', 'conversation'), verbose)
+    processor_config.conversation = ConversationProcessorConfigModel(config.processor.conversation, verbose)
 
     conversation_logfile = processor_config.conversation.conversation_logfile
     if processor_config.conversation.verbose:
@@ -180,12 +191,21 @@ def shutdown_event():
 if __name__ == '__main__':
     # Load config from CLI
     args = cli(sys.argv[1:])
+    
+    # Stores the file path to the config file.
+    config_file = args.config_file
 
-    # Initialize Search from Config
-    model, search_config = initialize_search(args.config, args.regenerate, args.verbose)
+    # Store the verbose flag
+    verbose = args.verbose
+
+    # Store the raw config data.
+    config = args.config
+
+    # Initialize the search model from Config
+    model = initialize_search(args.config, args.regenerate)
 
     # Initialize Processor from Config
-    processor_config = initialize_processor(args.config, args.verbose)
+    processor_config = initialize_processor(args.config)
 
     # Start Application Server
     if args.socket:

src/search_type/asymmetric.py

@@ -14,7 +14,8 @@ from sentence_transformers import SentenceTransformer, CrossEncoder, util
 # Internal Packages
 from src.utils.helpers import get_absolute_path, resolve_absolute_path
 from src.processor.org_mode.org_to_jsonl import org_to_jsonl
-from src.utils.config import TextSearchModel, TextSearchConfig
+from src.utils.config import TextSearchModel
+from src.utils.rawconfig import TextSearchConfig
 
 
 def initialize_model():
@@ -58,7 +59,7 @@ def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, v
         corpus_embeddings = bi_encoder.encode([entry[0] for entry in entries], convert_to_tensor=True, show_progress_bar=True)
         torch.save(corpus_embeddings, get_absolute_path(embeddings_file))
         if verbose > 0:
-            print(f"Computed embeddings and save them to {embeddings_file}")
+            print(f"Computed embeddings and saved them to {embeddings_file}")
 
     return corpus_embeddings
 
@@ -148,22 +149,22 @@ def collate_results(hits, entries, count=5):
         in hits[0:count]]
 
 
-def setup(config: TextSearchConfig, regenerate: bool) -> TextSearchModel:
+def setup(config: TextSearchConfig, regenerate: bool, verbose: bool) -> TextSearchModel:
     # Initialize Model
     bi_encoder, cross_encoder, top_k = initialize_model()
 
     # Map notes in Org-Mode files to (compressed) JSONL formatted file
     if not resolve_absolute_path(config.compressed_jsonl).exists() or regenerate:
-        org_to_jsonl(config.input_files, config.input_filter, config.compressed_jsonl, config.verbose)
+        org_to_jsonl(config.input_files, config.input_filter, config.compressed_jsonl, verbose)
 
     # Extract Entries
-    entries = extract_entries(config.compressed_jsonl, config.verbose)
+    entries = extract_entries(config.compressed_jsonl, verbose)
     top_k = min(len(entries), top_k)  # top_k hits can't be more than the total entries in corpus
 
     # Compute or Load Embeddings
-    corpus_embeddings = compute_embeddings(entries, bi_encoder, config.embeddings_file, regenerate=regenerate, verbose=config.verbose)
+    corpus_embeddings = compute_embeddings(entries, bi_encoder, config.embeddings_file, regenerate=regenerate, verbose=verbose)
 
-    return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, top_k, verbose=config.verbose)
+    return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, top_k, verbose=verbose)
 
 
 if __name__ == '__main__':

src/search_type/image_search.py

@@ -10,9 +10,10 @@ from tqdm import trange
 import torch
 
 # Internal Packages
-from src.utils.helpers import get_absolute_path, resolve_absolute_path
+from src.utils.helpers import resolve_absolute_path
 import src.utils.exiftool as exiftool
-from src.utils.config import ImageSearchModel, ImageSearchConfig
+from src.utils.config import ImageSearchModel
+from src.utils.rawconfig import ImageSearchConfig
 
 
 def initialize_model():
@@ -153,7 +154,7 @@ def collate_results(hits, image_names, image_directory, count=5):
         in hits[0:count]]
 
 
-def setup(config: ImageSearchConfig, regenerate: bool) -> ImageSearchModel:
+def setup(config: ImageSearchConfig, regenerate: bool, verbose: bool) -> ImageSearchModel:
     # Initialize Model
     encoder = initialize_model()
 
@@ -170,13 +171,13 @@ def setup(config: ImageSearchConfig, regenerate: bool) -> ImageSearchModel:
         batch_size=config.batch_size,
         regenerate=regenerate,
         use_xmp_metadata=config.use_xmp_metadata,
-        verbose=config.verbose)
+        verbose=verbose)
 
     return ImageSearchModel(image_names,
                             image_embeddings,
                             image_metadata_embeddings,
                             encoder,
-                            config.verbose)
+                            verbose)
 
 
 if __name__ == '__main__':

src/search_type/symmetric_ledger.py

@@ -1,9 +1,6 @@
 # Standard Packages
 import json
-import time
 import gzip
-import os
-import sys
 import re
 import argparse
 import pathlib
@@ -15,11 +12,12 @@ from sentence_transformers import SentenceTransformer, CrossEncoder, util
 # Internal Packages
 from src.utils.helpers import get_absolute_path, resolve_absolute_path
 from src.processor.ledger.beancount_to_jsonl import beancount_to_jsonl
-from src.utils.config import TextSearchModel, TextSearchConfig
+from src.utils.config import TextSearchModel
+from src.utils.rawconfig import TextSearchConfig
 
 
 def initialize_model():
-    "Initialize model for symetric semantic search. That is, where query of similar size to results"
+    "Initialize model for symmetric semantic search. That is, where query of similar size to results"
     torch.set_num_threads(4)
     bi_encoder = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') # The encoder encodes all entries to use for semantic search
     top_k = 30                                                                        # Number of entries we want to retrieve with the bi-encoder
@@ -55,7 +53,7 @@ def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, v
         corpus_embeddings = bi_encoder.encode(entries, convert_to_tensor=True, show_progress_bar=True)
         torch.save(corpus_embeddings, get_absolute_path(embeddings_file))
         if verbose > 0:
-            print(f"Computed embeddings and save them to {embeddings_file}")
+            print(f"Computed embeddings and saved them to {embeddings_file}")
 
     return corpus_embeddings
 
@@ -143,7 +141,7 @@ def collate_results(hits, entries, count=5):
         in hits[0:count]]
 
 
-def setup(config: TextSearchConfig, regenerate: bool) -> TextSearchModel:
+def setup(config: TextSearchConfig, regenerate: bool, verbose: bool) -> TextSearchModel:
     # Initialize Model
     bi_encoder, cross_encoder, top_k = initialize_model()
 
@@ -156,9 +154,9 @@ def setup(config: TextSearchConfig, regenerate: bool) -> TextSearchModel:
     top_k = min(len(entries), top_k)
 
     # Compute or Load Embeddings
-    corpus_embeddings = compute_embeddings(entries, bi_encoder, config.embeddings_file, regenerate=regenerate, verbose=config.verbose)
+    corpus_embeddings = compute_embeddings(entries, bi_encoder, config.embeddings_file, regenerate=regenerate, verbose=verbose)
 
-    return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, top_k, verbose=config.verbose)
+    return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, top_k, verbose=verbose)
 
 
 if __name__ == '__main__':

src/utils/cli.py

@@ -1,12 +1,14 @@
 # Standard Packages
 import argparse
 import pathlib
+import json
 
 # External Packages
 import yaml
 
 # Internal Packages
-from src.utils.helpers import is_none_or_empty, get_absolute_path, resolve_absolute_path, get_from_dict, merge_dicts
+from src.utils.helpers import is_none_or_empty, get_absolute_path, resolve_absolute_path, merge_dicts
+from src.utils.rawconfig import FullConfig
 
 def cli(args=None):
     if is_none_or_empty(args):
@@ -35,12 +37,15 @@ def cli(args=None):
         with open(get_absolute_path(args.config_file), 'r', encoding='utf-8') as config_file:
             config_from_file = yaml.safe_load(config_file)
             args.config = merge_dicts(priority_dict=config_from_file, default_dict=args.config)
+            args.config = FullConfig.parse_obj(args.config)
+    else:
+        args.config = FullConfig.parse_obj(args.config)
 
     if args.org_files:
-        args.config['content-type']['org']['input-files'] = args.org_files
+        args.config.content_type.org.input_files = args.org_files
 
     if args.org_filter:
-        args.config['content-type']['org']['input-filter'] = args.org_filter
+        args.config.content_type.org.input_filter = args.org_filter
 
     return args
 

src/utils/config.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 from pathlib import Path
 
 # Internal Packages
-from src.utils.helpers import get_from_dict
+from src.utils.rawconfig import ConversationProcessorConfig
 
 
 class SearchType(str, Enum):
@@ -42,80 +42,15 @@ class SearchModels():
     image_search: ImageSearchModel = None
 
 
-class TextSearchConfig():
-    def __init__(self, input_files, input_filter, compressed_jsonl, embeddings_file, verbose):
-        self.input_files = input_files
-        self.input_filter = input_filter
-        self.compressed_jsonl = Path(compressed_jsonl)
-        self.embeddings_file = Path(embeddings_file)
+class ConversationProcessorConfigModel():
+    def __init__(self, processor_config: ConversationProcessorConfig, verbose: bool):
+        self.openai_api_key = processor_config.open_api_key
+        self.conversation_logfile = Path(processor_config.conversation_logfile)
+        self.chat_log = ''
+        self.meta_log = []
+        self.conversation_history = Path(processor_config.conversation_history)
         self.verbose = verbose
 
-
-    def create_from_dictionary(config, key_tree, verbose):
-        text_config = get_from_dict(config, *key_tree)
-        search_enabled = text_config and ('input-files' in text_config or 'input-filter' in text_config)
-        if not search_enabled:
-            return None
-
-        return TextSearchConfig(
-            input_files = text_config['input-files'],
-            input_filter = text_config['input-filter'],
-            compressed_jsonl = Path(text_config['compressed-jsonl']),
-            embeddings_file = Path(text_config['embeddings-file']),
-            verbose = verbose)
-
-
-class ImageSearchConfig():
-    def __init__(self, input_directory, embeddings_file, batch_size, use_xmp_metadata, verbose):
-        self.input_directory = input_directory
-        self.embeddings_file = Path(embeddings_file)
-        self.batch_size = batch_size
-        self.use_xmp_metadata = use_xmp_metadata
-        self.verbose = verbose
-
-    def create_from_dictionary(config, key_tree, verbose):
-        image_config = get_from_dict(config, *key_tree)
-        search_enabled = image_config and 'input-directory' in image_config
-        if not search_enabled:
-            return None
-
-        return ImageSearchConfig(
-            input_directory = Path(image_config['input-directory']),
-            embeddings_file = Path(image_config['embeddings-file']),
-            batch_size = image_config['batch-size'],
-            use_xmp_metadata = {'yes': True, 'no': False}[image_config['use-xmp-metadata']],
-            verbose = verbose)
-
-
 @dataclass
-class SearchConfig():
-    notes: TextSearchConfig = None
-    ledger: TextSearchConfig = None
-    music: TextSearchConfig = None
-    image: ImageSearchConfig = None
-
-
-class ConversationProcessorConfig():
-    def __init__(self, conversation_logfile, chat_log, meta_log, openai_api_key, verbose):
-        self.openai_api_key = openai_api_key
-        self.conversation_logfile = conversation_logfile
-        self.chat_log = chat_log
-        self.meta_log = meta_log
-        self.verbose = verbose
-
-    def create_from_dictionary(config, key_tree, verbose):
-        conversation_config = get_from_dict(config, *key_tree)
-        if not conversation_config:
-            return None
-
-        return ConversationProcessorConfig(
-            openai_api_key = conversation_config['openai-api-key'],
-            chat_log = '',
-            meta_log = [],
-            conversation_logfile = Path(conversation_config['conversation-logfile']),
-            verbose = verbose)
-
-
-@dataclass
-class ProcessorConfig():
-    conversation: ConversationProcessorConfig = None
+class ProcessorConfigModel():
+    conversation: ConversationProcessorConfigModel = None

src/utils/helpers.py

@@ -4,6 +4,8 @@ import pathlib
 def is_none_or_empty(item):
     return item == None or (hasattr(item, '__iter__') and len(item) == 0)
 
+def to_snake_case_from_dash(item: str):
+    return item.replace('_', '-')
 
 def get_absolute_path(filepath):
     return str(pathlib.Path(filepath).expanduser().absolute())

src/utils/rawconfig.py

@@ -0,0 +1,62 @@
+# System Packages
+from pathlib import Path
+from typing import List, Optional
+
+# External Packages
+from pydantic import BaseModel
+
+# Internal Packages
+from src.utils.helpers import to_snake_case_from_dash
+
+class ConfigBase(BaseModel):
+    class Config:
+        alias_generator = to_snake_case_from_dash
+        allow_population_by_field_name = True
+
+class SearchConfig(ConfigBase):
+    input_files: Optional[List[str]]
+    input_filter: Optional[str]
+    embeddings_file: Optional[Path]
+
+class TextSearchConfig(ConfigBase):
+    compressed_jsonl: Optional[Path]
+    input_files: Optional[List[str]]
+    input_filter: Optional[str]
+    embeddings_file: Optional[Path]
+
+class ImageSearchConfig(ConfigBase):
+    use_xmp_metadata: Optional[str]
+    batch_size: Optional[int]
+    input_directory: Optional[Path]
+    input_filter: Optional[str]
+    embeddings_file: Optional[Path]
+
+class ContentTypeConfig(ConfigBase):
+    org: Optional[TextSearchConfig]
+    ledger: Optional[TextSearchConfig]
+    image: Optional[ImageSearchConfig]
+    music: Optional[TextSearchConfig]
+
+class AsymmetricConfig(ConfigBase):
+    encoder: Optional[str]
+    cross_encoder: Optional[str]
+
+class ImageSearchTypeConfig(ConfigBase):
+    encoder: Optional[str]
+
+class SearchTypeConfig(ConfigBase):
+    asymmetric: Optional[AsymmetricConfig]
+    image: Optional[ImageSearchTypeConfig]
+
+class ConversationProcessorConfig(ConfigBase):
+    open_api_key: Optional[str]
+    conversation_logfile: Optional[str]
+    conversation_history: Optional[str]
+
+class ProcessorConfigModel(ConfigBase):
+    conversation: Optional[ConversationProcessorConfig]
+
+class FullConfig(ConfigBase):
+    content_type: Optional[ContentTypeConfig]
+    search_type: Optional[SearchTypeConfig]
+    processor: Optional[ProcessorConfigModel]

tests/conftest.py

@@ -3,8 +3,8 @@ import pytest
 from pathlib import Path
 
 # Internal Packages
-from src.utils.config import SearchConfig, TextSearchConfig, ImageSearchConfig
 from src.search_type import asymmetric, image_search
+from src.utils.rawconfig import ContentTypeConfig, ImageSearchConfig, TextSearchConfig
 
 
 @pytest.fixture(scope='session')
@@ -12,44 +12,40 @@ def model_dir(tmp_path_factory):
     model_dir = tmp_path_factory.mktemp('data')
 
     # Generate Image Embeddings from Test Images
-    search_config = SearchConfig()
+    search_config = ContentTypeConfig()
     search_config.image = ImageSearchConfig(
         input_directory = Path('tests/data'),
         embeddings_file = model_dir.joinpath('.image_embeddings.pt'),
         batch_size = 10,
-        use_xmp_metadata = False,
-        verbose = 2)
+        use_xmp_metadata = False)
 
-    image_search.setup(search_config.image, regenerate=False)
+    image_search.setup(search_config.image, regenerate=False, verbose=True)
 
     # Generate Notes Embeddings from Test Notes
-    search_config.notes = TextSearchConfig(
+    search_config.org = TextSearchConfig(
         input_files = [Path('tests/data/main_readme.org'), Path('tests/data/interface_emacs_readme.org')],
         input_filter = None,
         compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'),
-        embeddings_file = model_dir.joinpath('.note_embeddings.pt'),
-        verbose = 0)
+        embeddings_file = model_dir.joinpath('.note_embeddings.pt'))
 
-    asymmetric.setup(search_config.notes, regenerate=False)
+    asymmetric.setup(search_config.notes, regenerate=False, verbose=True)
 
     return model_dir
 
 
 @pytest.fixture(scope='session')
 def search_config(model_dir):
-    search_config = SearchConfig()
-    search_config.notes = TextSearchConfig(
+    search_config = ContentTypeConfig()
+    search_config.org = TextSearchConfig(
         input_files = [Path('tests/data/main_readme.org'), Path('tests/data/interface_emacs_readme.org')],
         input_filter = None,
         compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'),
-        embeddings_file = model_dir.joinpath('.note_embeddings.pt'),
-        verbose = 2)
+        embeddings_file = model_dir.joinpath('.note_embeddings.pt'))
 
     search_config.image = ImageSearchConfig(
         input_directory = Path('tests/data'),
         embeddings_file = Path('tests/data/.image_embeddings.pt'),
         batch_size = 10,
-        use_xmp_metadata = False,
-        verbose = 2)
+        use_xmp_metadata = False)
 
     return search_config

tests/test_asymmetric_search.py

@@ -8,7 +8,7 @@ from src.search_type import asymmetric
 def test_asymmetric_setup(search_config):
     # Act
     # Regenerate notes embeddings during asymmetric setup
-    notes_model = asymmetric.setup(search_config.notes, regenerate=True)
+    notes_model = asymmetric.setup(search_config.org, regenerate=True)
 
     # Assert
     assert len(notes_model.entries) == 10
@@ -18,7 +18,7 @@ def test_asymmetric_setup(search_config):
 # ----------------------------------------------------------------------------------------------------
 def test_asymmetric_search(search_config):
     # Arrange
-    model.notes_search = asymmetric.setup(search_config.notes, regenerate=False)
+    model.notes_search = asymmetric.setup(search_config.org, regenerate=False)
     query = "How to git install application?"
 
     # Act

tests/test_cli.py

@@ -40,7 +40,7 @@ def test_cli_config_from_file():
     assert actual_args.config_file == Path('tests/data/config.yml')
     assert actual_args.regenerate == True
     assert actual_args.config is not None
-    assert actual_args.config['content-type']['org']['input-files'] == ['~/first_from_config.org', '~/second_from_config.org']
+    assert actual_args.config.content_type.org.input_files == ['~/first_from_config.org', '~/second_from_config.org']
     assert actual_args.verbose == 3
 
 
@@ -54,7 +54,7 @@ def test_cli_config_from_cmd_args():
     assert actual_args.org_files == ['first.org']
     assert actual_args.config_file is None
     assert actual_args.config is not None
-    assert actual_args.config['content-type']['org']['input-files'] == ['first.org']
+    assert actual_args.config.content_type.org.input_files == ['first.org']
 
 
 # ----------------------------------------------------------------------------------------------------
@@ -67,4 +67,4 @@ def test_cli_config_from_cmd_args_override_config_file():
     assert actual_args.org_files == ['first.org']
     assert actual_args.config_file == Path('tests/data/config.yml')
     assert actual_args.config is not None
-    assert actual_args.config['content-type']['org']['input-files'] == ['first.org']
+    assert actual_args.config.content_type.org.input_files == ['first.org']

tests/test_client.py

@@ -5,15 +5,16 @@ from pathlib import Path
 from fastapi.testclient import TestClient
 
 # Internal Packages
-from src.main import app, model, search_config as main_search_config
+from src.main import app, model, config
 from src.search_type import asymmetric, image_search
 from src.utils.helpers import resolve_absolute_path
+from src.utils.rawconfig import FullConfig
 
 
 # Arrange
 # ----------------------------------------------------------------------------------------------------
 client = TestClient(app)
-
+config = FullConfig()
 
 # Test
 # ----------------------------------------------------------------------------------------------------
@@ -31,7 +32,7 @@ def test_search_with_invalid_search_type():
 # ----------------------------------------------------------------------------------------------------
 def test_search_with_valid_search_type(search_config):
     # Arrange
-    main_search_config.image = search_config.image
+    config.content_type.image = search_config.image
     for search_type in ["notes", "ledger", "music", "image"]:
         # Act
         response = client.get(f"/search?q=random&t={search_type}")
@@ -51,7 +52,7 @@ def test_regenerate_with_invalid_search_type():
 # ----------------------------------------------------------------------------------------------------
 def test_regenerate_with_valid_search_type(search_config):
     # Arrange
-    main_search_config.image = search_config.image
+    config.content_type.image = search_config.image
     for search_type in ["notes", "ledger", "music", "image"]:
         # Act
         response = client.get(f"/regenerate?t={search_type}")
@@ -62,7 +63,7 @@ def test_regenerate_with_valid_search_type(search_config):
 # ----------------------------------------------------------------------------------------------------
 def test_image_search(search_config):
     # Arrange
-    main_search_config.image = search_config.image
+    config.content_type.image = search_config.image
     model.image_search = image_search.setup(search_config.image, regenerate=False)
     query_expected_image_pairs = [("brown kitten next to fallen plant", "kitten_park.jpg"),
                                   ("a horse and dog on a leash", "horse_dog.jpg"),

views/config.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+    <head>
+        <title>Set directories for your config file.</title>
+        <link rel="stylesheet" href="views/style.css">
+    </head>
+    <body>
+        <form id="config-form">
+        </form>
+        <button id="config-regenerate">regenerate</button>
+    </body>
+    <script src="views/scripts/config.js"></script>
+</html>

views/scripts/config.js

@@ -0,0 +1,124 @@
+// Retrieve elements from the DOM.
+var showConfig = document.getElementById("show-config");
+var configForm = document.getElementById("config-form");
+var regenerateButton = document.getElementById("config-regenerate");
+
+// Global variables.
+var rawConfig = {};
+var emptyValueDefault = "🖊️";
+
+/**
+ * Fetch the existing config file.
+ */
+fetch("/config")
+    .then(response => response.json())
+    .then(data => {
+        rawConfig = data;
+        configForm.style.display = "block";
+        processChildren(configForm, data);
+
+        var submitButton = document.createElement("button");
+        submitButton.type = "submit";
+        submitButton.innerHTML = "update";
+        configForm.appendChild(submitButton);
+
+        // The config form's submit handler.
+        configForm.addEventListener("submit", (event) => {
+            event.preventDefault();
+            console.log(rawConfig);
+            const response = fetch("/config", {
+                method: "POST",
+                credentials: "same-origin",
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify(rawConfig)
+            }).then(response => response.json())
+            .then((data) => console.log(data));
+        });
+});
+
+/**
+ * The click handler for the Regenerate button.
+ */
+regenerateButton.addEventListener("click", (event) => {
+    event.preventDefault();
+    regenerateButton.style.cursor = "progress";
+    regenerateButton.disabled = true;
+    fetch("/regenerate")
+        .then(response => response.json())
+        .then(data => {
+            regenerateButton.style.cursor = "pointer";
+            regenerateButton.disabled = false;
+            console.log(data);
+        });
+})
+
+/**
+ * Adds config elements to the DOM representing the sub-components 
+ * of one of the fields in the raw config file.
+ * @param {the parent element} element 
+ * @param {the data to be rendered for this element and its children} data 
+ */
+function processChildren(element, data) {
+    for (let key in data) {
+        var child = document.createElement("div");
+        child.id = key;
+        child.className = "config-element";
+        child.appendChild(document.createTextNode(key + ": "));
+        if (data[key] === Object(data[key]) && !Array.isArray(data[key])) {
+            child.className+=" config-title";
+            processChildren(child, data[key]);
+        } else {
+            child.appendChild(createValueNode(data, key));
+        }
+        element.appendChild(child);
+    }
+}
+
+/**
+ * Takes an element, and replaces it with an editable 
+ * element with the same data in place.
+ * @param {the original element to be replaced} original 
+ * @param {the source data to be rendered for the new element} data 
+ * @param {the key for this input in the source data} key 
+ */
+function makeElementEditable(original, data, key) {
+    original.addEventListener("click", () => {
+        var inputNewText = document.createElement("input");
+        inputNewText.type = "text";
+        inputNewText.className = "config-element-edit";
+        inputNewText.value = (original.textContent == emptyValueDefault) ? "" : original.textContent;
+        fixInputOnFocusOut(inputNewText, data, key);
+        original.parentNode.replaceChild(inputNewText, original);
+        inputNewText.focus();
+    });
+}
+
+/**
+ * Creates a node corresponding to the value of a config element.
+ * @param {the source data} data 
+ * @param {the key corresponding to this node's data} key 
+ * @returns A new element which corresponds to the value in some field.
+ */
+function createValueNode(data, key) {
+    var valueElement = document.createElement("span");
+    valueElement.className = "config-element-value";
+    valueElement.textContent = !data[key] ? emptyValueDefault : data[key];
+    makeElementEditable(valueElement, data, key);
+    return valueElement;
+}
+
+/**
+ * Replaces an existing input element with an element with the same data, which is not an input. 
+ * If the input data for this element was changed, update the corresponding data in the raw config.
+ * @param {the original element to be replaced} original 
+ * @param {the source data} data 
+ * @param {the key corresponding to this node's data} key 
+ */
+function fixInputOnFocusOut(original, data, key) {
+    original.addEventListener("blur", () => {
+        data[key] = (original.value != emptyValueDefault) ? original.value : "";
+        original.parentNode.replaceChild(createValueNode(data, key), original);
+    })
+}

views/style.css

@@ -0,0 +1,29 @@
+:root {
+    --primary-color: #ffffff;
+    --bold-color: #2073ee;
+    --complementary-color: #124408;
+    --accent-color-0: #57f0b5;
+}
+
+input[type=text] {
+    width: 40%;
+}
+
+div.config-element {
+    color: var(--bold-color);
+    margin: 8px;
+}
+
+div.config-title {
+    font-weight: bold;
+}
+
+span.config-element-value {
+    color: var(--complementary-color);
+    font-weight: normal;
+    cursor: pointer;
+}
+
+button {
+    cursor: pointer;
+}