feat(main): add paragraph db to CLI

knowledge_verificator/main.py

@@ -4,60 +4,123 @@
 
 from knowledge_verificator.io_handler import logger, console
 from knowledge_verificator.answer_chooser import AnswerChooser
+from knowledge_verificator.materials import MaterialDatabase
 from knowledge_verificator.nli import NaturalLanguageInference, Relation
 from knowledge_verificator.qg import QuestionGeneration
 
 
 if __name__ == '__main__':
-    chooser = AnswerChooser()
     qg_module = QuestionGeneration()
+    chooser = AnswerChooser()
+
+    while True:
+        console.print('Where you want to learn from?')
+        console.print('[1] knowledge database')
+        console.print('[2] my own paragraph')
+        user_choice = input('Your choice: ')
+        console.print()
+
+        match user_choice:
+            case '1':
+                try:
+                    DB_PATH = './data'
+                    material_db = MaterialDatabase(DB_PATH)
+                except FileNotFoundError:
+                    console.print(
+                        f'In the `{DB_PATH}` there is no database. '
+                        'Try using your own materials.'
+                    )
+                    continue
+
+                if not material_db.materials:
+                    console.print(
+                        'The knowledge database exists but is empty. '
+                        'Try using your own materials.'
+                    )
+                    continue
+
+                console.print('Available materials:')
+                for i, material in enumerate(material_db.materials):
+                    console.print(f'[{i+1}] {material.title}')
+                material_choice = input('Your choice: ')
+                console.print()
+
+                INCORRECT_CHOICE_WARNING = (
+                    'This is incorrect choice. Next time, provide a number '
+                    'next to a material from the list of available ones.'
+                )
+                if not material_choice.isnumeric():
+                    console.print(INCORRECT_CHOICE_WARNING)
+                    continue
+
+                chosen_index = int(material_choice) - 1
+                if chosen_index < 0 or chosen_index >= len(
+                    material_db.materials
+                ):
+                    console.print(INCORRECT_CHOICE_WARNING)
+                    continue
+
+                material = material_db.materials[chosen_index]
+                # TODO: A user should be able to choose a paragraph.
+                paragraph = material.paragraphs[0]
+
+                console.print('Learn this paragraph: ')
+                console.print(paragraph)
+                console.print()
+                input('Press ENTER when ready.')
+
+            case '2':
+                console.print('Enter a paragraph you would like to learn: ')
+                paragraph = input().strip()
 
-    console.print('Enter a paragraph you would like to learn: ')
-    paragraph = input().strip()
-    logger.debug('Loaded the following paragraph:\n %s', paragraph)
+            case _:
+                console.print('Unrecognised option, try again!')
 
-    chosen_answer = chooser.choose_answer(paragraph=paragraph)
-    if not chosen_answer:
-        raise ValueError(
-            'The supplied paragaph is either too short or too general. '
-            'Please, try providing a longer or more specific paragraph.'
+        logger.debug('Loaded the following paragraph:\n %s', paragraph)
+
+        chosen_answer = chooser.choose_answer(paragraph=paragraph)
+        if not chosen_answer:
+            raise ValueError(
+                'The supplied paragaph is either too short or too general. '
+                'Please, try providing a longer or more specific paragraph.'
+            )
+
+        console.clear()
+
+        logger.debug(
+            'The `%s` has been chosen as the answer, based on which question '
+            'will be generated.',
+            chosen_answer,
+        )
+
+        question_with_context = qg_module.generate(
+            answer=chosen_answer, context=paragraph
+        )
+        question = question_with_context['question']
+        logger.debug(
+            'Question Generation module has supplied the question: %s', question
         )
 
-    console.clear()
-
-    logger.debug(
-        'The `%s` has been chosen as the answer, based on which the question will be generated.',
-        chosen_answer,
-    )
-
-    question_with_context = qg_module.generate(
-        answer=chosen_answer, context=paragraph
-    )
-    question = question_with_context['question']
-    logger.debug(
-        'Question Generation module has supplied the question: %s', question
-    )
-
-    console.print(
-        f'\nAnswer the question with full sentence. {question} \nYour answer: '
-    )
-    user_answer = input().strip()
-
-    nli_module = NaturalLanguageInference()
-    relation = nli_module.infer_relation(
-        premise=paragraph, hypothesis=user_answer
-    )
-
-    match relation:
-        case Relation.ENTAILMENT:
-            FEEDBACK = 'correct'
-            STYLE = 'green'
-        case Relation.CONTRADICTION:
-            FEEDBACK = f'wrong. Correct answer is {chosen_answer}'
-            STYLE = 'red'
-        case Relation.NEUTRAL:
-            FEEDBACK = 'not directly associated with the posed question'
-            STYLE = 'yellow'
-
-    feedback_text = Text(f'Your answer is {FEEDBACK}.', style=STYLE)
-    console.print(feedback_text)
+        console.print(
+            f'\nAnswer the question with full sentence. {question} \nYour answer: '
+        )
+        user_answer = input().strip()
+
+        nli_module = NaturalLanguageInference()
+        relation = nli_module.infer_relation(
+            premise=paragraph, hypothesis=user_answer
+        )
+
+        match relation:
+            case Relation.ENTAILMENT:
+                FEEDBACK = 'correct'
+                STYLE = 'green'
+            case Relation.CONTRADICTION:
+                FEEDBACK = f'wrong. Correct answer is {chosen_answer}'
+                STYLE = 'red'
+            case Relation.NEUTRAL:
+                FEEDBACK = 'not directly associated with the posed question'
+                STYLE = 'yellow'
+
+        feedback_text = Text(f'Your answer is {FEEDBACK}.', style=STYLE)
+        console.print(feedback_text)