python “Use of super on an old style class”的解决办法

本文详细解析了Python中老样式和新样式类继承的两种不同写法,对比了使用super函数与类名直接调用基类方法的区别,并介绍了这两种写法如何避免基类构造函数的重复调用。

在调试代码时,发现引用的第三方架包突然提示如下错误:

E1002:<line,row>:Employee.__init__: Use of super on an old style class

仔细诊断代码,发现相关的内容如下:

class Base:
    def __init__(self, username, age):
        self.username = username
        self.age = age


class Employee(Base):
    #   在这里提示错误,Use of super on an old style class
    def __init__(self, username, age):
        super(Employee, self).__init__(username, age)

强行运行,又提示如下错误:

File "e:/Python/mirana-python/ClassExtend.py", line 15, in __init__
    super(Employee, self).__init__(self, username, age)
    TypeError: super() argument 1 must be type, not classobj

查询相关资料,发现Python类的继承有两种写法,一是老样式(old style),一是新样式(new style),都能解决上述的错误。

1. 老写法(old style)

老写法不能使用super函数,必须通过类名进行调用,这样的问题就在于,出现多重继承时,应该说多层继承更为准确,A是基类,B继承A,C继承A,D继承B、C,这样在D被初始化时,就可能出现A的初始化函数被调用多次,写法如下:

#   old style
class Base:
    def __init__(self, username, age):
        self.username = username
        self.age = age


class Employee(Base):
    def __init__(self, username, age):
        #   通过类名进行调用,必须携带self
        Base.__init__(self, username, age)

2. 新写法(new style)

与老写法的区别在于,一是基类必须继承object,二是调用父类的方法时必须采用super方法,当然它也能解决老写法的重复调用问题,是现在面向对象编程的推荐写法,如下:

#   第一处差异,必须继承object
class Base(object):
    def __init__(self, username, age):
        self.username = username
        self.age = age


class Employee(Base):
    def __init__(self, username, age):
        # 第二处差异,必须使用super函数,并且省略self参数
        super(Employee, self).__init__(username, age)

结论

python面向对象编程实现继承有两种写法,一是老写法,二是新写法,明白了它们的差异,就能写出更高效、扩展性更强的代码。

<think>We are creating a template for a female character profile with dialogue samples. This should be modular and scalable for use in an app. We'll define a structure that includes: - Basic character info (name, age, appearance, personality traits, etc.) - Dialogue samples (with context and variations) - Narrator rules (how the narrator should describe the character's actions, thoughts, etc.) - Writing style guidelines (to maintain consistency in the character's voice) We'll use a class-based approach in Python to encapsulate the character profile. This allows for creating multiple characters and extending the functionality. Steps: 1. Define a class `CharacterProfile` that holds all the character information. 2. Use dictionaries or lists for traits, dialogue samples, etc. to allow for multiple entries. 3. For dialogue samples, we can have a list of dictionaries where each dictionary represents a dialogue sample with: - context: the situation in which the dialogue is spoken - lines: a list of possible lines the character might say in that context 4. Narrator rules can be a set of guidelines (as strings) or even functions that generate descriptions based on the situation (if we want to get advanced). 5. Writing style guidelines can be a list of rules (strings) that define the character's voice. However, note: the user asked for a template for creating a profile, so we are not building the entire app but the first part (a modular and scalable template). We'll design: class CharacterProfile: name: str age: int appearance: dict # e.g., {'hair': 'blonde', 'eyes': 'blue', ...} personality_traits: list # e.g., ['kind', 'intelligent', ...] dialogue_samples: list of dicts # each dict: {'context': str, 'lines': list of str} narrator_rules: list of str # rules for the narrator writing_style: list of str # guidelines for writing the character's dialogue and actions We can also have methods to add dialogue samples, etc. Example of a dialogue sample: { "context": "Greeting when meeting someone for the first time", "lines": [ "Hi, I'm [Name]. Nice to meet you!", "Hello there. My name is [Name], and you are?", ... ] } But note: we want to avoid hardcoding the name. We can use placeholders and then replace them when needed. However, for the template, we can have a placeholder like `{name}` that gets replaced when the line is used. Alternatively, we can have a method that formats a line with the character's attributes. Let's design: def get_dialogue(self, context_key, **replacements): # Find the dialogue for the given context, then randomly choose a line and format it with replacements and the character's own attributes. But note: the user asked for a template for the profile, so we might not need the logic for now. We can just store the data. However, for scalability, we can include helper methods. Given the requirement for modularity, we might want to separate the data from the logic. But for the first part, we can have a basic class. Alternatively, we can design the profile as a data class and then have a separate module for processing (like a dialogue engine) later. Since the request is for a template that can be the first part of an entire app, I will design a class that holds the data and has some basic methods for adding and retrieving information. Let's create: class CharacterProfile: def __init__(self, name, age, ...): self.name = name self.age = age self.appearance = {} self.personality_traits = [] self.dialogue_samples = [] # list of DialogueSample objects? or just dicts? Let's create a nested class for dialogue sample for structure. We can define a nested class for dialogue sample, but for simplicity and JSON serialization (if needed) we might use dictionaries. However, for better structure, we can have: class DialogueSample: def __init__(self, context, lines): self.context = context self.lines = lines # list of strings Then dialogue_samples would be a list of DialogueSample instances. But then we lose the simplicity of using dictionaries for serialization. Alternatively, we can use namedtuples or dataclasses. Since we are using Python, we can use dataclasses for the DialogueSample to make it clean. Let's use dataclasses if we are in Python 3.7+. If we want to support older versions, we can use simple classes. But note: the user didn't specify the language. However, the question is tagged under programming and we are to give code. Since the problem is about creating a template for a character profile, and we are to make it modular and scalable, I choose Python because it's often used for such tasks and is readable. We'll assume Python 3.7+. Alternatively, we can avoid dataclasses and use dictionaries to keep it simple and flexible. Considering the requirement for modularity and scalability, I will design with classes and then if we need serialization we can add to_dict and from_dict methods. Steps: We'll create: class DialogueSample: context: str lines: List[str] Then in CharacterProfile, we have: dialogue_samples: List[DialogueSample] We also want to be able to add a dialogue sample. Similarly for narrator_rules and writing_style, we can have lists of strings. We'll also include a method to get a random line for a given context. We'll include a method to get a random line that matches a context (if there are multiple samples for the same context, we might want to merge them? or we allow multiple samples per context? Actually, we can have multiple samples for the same context and then we randomly pick one of them and then randomly pick a line from that sample? Not exactly. Instead, we might want to have one context per sample and then if we have duplicate contexts, we combine the lines? Or we can have the context as a key? But that requires a mapping. Alternatively, we can store dialogue_samples as a dictionary where the key is the context and the value is a list of lines? Then we can have: self.dialogue_samples = { "context1": ["line1", "line2", ...], "context2": [ ... ], } Then adding a dialogue sample for an existing context would extend the list. This might be easier. So let's change: Instead of a list of DialogueSample, we can have a dictionary: self.dialogue_samples = {} Then we have a method: def add_dialogue_sample(self, context, lines): if context not in self.dialogue_samples: self.dialogue_samples[context] = [] self.dialogue_samples[context].extend(lines) Then to get a random line for a context: import random def get_dialogue_line(self, context): if context in self.dialogue_samples and self.dialogue_samples[context]: return random.choice(self.dialogue_samples[context]) else: return None # or raise an exception But what if we want to have more metadata per dialogue sample? For example, the emotion or the situation? Then a dictionary with context as key might be too simplistic. We might have multiple contexts that are actually the same in meaning but different keys? Then we need to normalize. Considering scalability, we might need a more advanced structure. But for the first part, let's keep it simple. We'll go with a dictionary for dialogue_samples for now. Similarly, for appearance, we can have a dictionary. Personality traits are a list. We'll also include: self.narrator_rules = [] # list of rules (strings) for the narrator self.writing_style = [] # list of guidelines (strings) for writing And methods to add to these. Example usage: character = CharacterProfile(name="Alice", age=25) character.set_appearance(hair="blonde", eyes="blue") character.add_personality_trait("kind") character.add_dialogue_sample(context="greeting", lines=["Hello!", "Hi there!"]) character.add_narrator_rule("Always describe her smile as warm and inviting.") character.add_writing_style_guideline("Use contractions to make the dialogue sound natural.") We can also have a __str__ method to print the profile. But note: the user might want to use this in an app, so we might later serialize to JSON or a database. We can add a to_dict method and a from_dict method. Let's design: def to_dict(self): return { "name": self.name, "age": self.age, "appearance": self.appearance, "personality_traits": self.personality_traits, "dialogue_samples": self.dialogue_samples, # which is a dict of context: lines "narrator_rules": self.narrator_rules, "writing_style": self.writing_style } @classmethod def from_dict(cls, data): character = cls(data["name"], data["age"]) character.appearance = data["appearance"] character.personality_traits = data["personality_traits"] character.dialogue_samples = data["dialogue_samples"] character.narrator_rules = data["narrator_rules"] character.writing_style = data["writing_style"] return character We can also store additional fields. We can make the __init__ accept arbitrary fields? But let's stick to defined attributes for now. Alternatively, we can have a dictionary for additional attributes? But we want to be explicit. We'll stick to the defined attributes. Implementation: We'll create the class with: - __init__ that initializes the attributes - Methods to add: appearance (we can update the appearance dict), personality_traits (append), dialogue_sample (add to the dictionary context), narrator_rule (append), writing_style_guideline (append) - get_dialogue_line(context) -> returns a random line for that context We'll also consider that we might want to get a line and replace placeholders. For example, if a line is "Hello, I'm {name}", we want to replace with the character's name. We can have a method: def get_formatted_dialogue_line(self, context, **kwargs): line = self.get_dialogue_line(context) if line is None: return None # Format the line: first replace with character attributes, then with kwargs # We can use the character's attributes as a dictionary and update it with kwargs? format_dict = self.to_dict() # But note: to_dict returns a lot and might have nested structures. We don't want to use the whole dict because if a line has "{appearance}", it would try to format with the appearance dict -> which might cause an error. Instead, we can define a context for formatting that includes the character's basic attributes (like name, age) and also the kwargs. We can create a formatting dictionary: base_dict = { "name": self.name, "age": self.age, # we can also add appearance? But appearance is a dict. We can flatten it? Or we can do: "appearance": self.appearance, # then in the string we can do "{appearance['hair']}" -> but that requires the string to include that, which might be complex. } Alternatively, we can only allow a set of top-level attributes and then the kwargs. We can do: base_dict = {"name": self.name, "age": self.age} base_dict.update(self.appearance) # This flattens the appearance. But what if there are conflicts? e.g., if we have an appearance key "age", it would overwrite? So we have to be cautious. Instead, we can prefix the appearance keys? But that complicates. Another idea: we can have two steps: first replace the kwargs, then replace the character attributes? Or we can have a single dictionary that has: context_dict = { "character": { "name": self.name, "age": self.age, "appearance": self.appearance, "personality": self.personality_traits # but how would they use this? They might want to use a random trait? Then better to have a method that selects one randomly? }, **kwargs } Then the line can include: "Hello! I'm {character.name} and I have {character.appearance.hair} hair." -> but this requires using dot access in formatting. We can use advanced string formatting: line.format(character=self, **kwargs) But then the line would look like: "Hello, I'm {character.name}. Today I feel {mood}." # and then we pass mood in kwargs. And we define the character parameter as self. Then in the line, we can use: line.format(character=self, **kwargs) But note: we don't want to expose all of self in the formatting because it might have methods, etc. Alternatively, we can create a dictionary representation with only the data we want: format_dict = { "name": self.name, "age": self.age, "appearance": self.appearance, # now the line can use {appearance['hair']} but that is not very dot-style. } And then update with kwargs. But then if the line is: "My hair is {appearance['hair']}." Then: line.format(**format_dict, **kwargs) This would work. But the user might want to use dot notation. We can use a wrapper object that allows dot access? We can do: from types import SimpleNamespace char_namespace = SimpleNamespace(name=self.name, age=self.age, appearance=SimpleNamespace(**self.appearance)) Then the line would be: "My hair is {character.appearance.hair}." And we pass: line.format(character=char_namespace, **kwargs) This is more user-friendly but more work. Given the complexity, and that the user might write the lines with the expectation of having the character's attributes, we can decide on a format. Since we are building a template, we can choose: We will use a formatting context that includes: - a dictionary `character` that has the following keys: name, age, and then the appearance as a nested dictionary? But then how to access in string? We can't do dot notation. Alternatively, we can predefine common attributes at the top level and then for appearance, we can flatten with a prefix? For example: "My hair is {hair}." Then when formatting, we do: base_dict = { "name": self.name, "age": self.age, **self.appearance # but then if appearance has a key "name", it would override -> so we avoid that by choosing appearance keys that don't conflict? Or we don't put name and age in appearance. We can have: base_dict = { "name": self.name, "age": self.age, "hair": self.appearance.get("hair", ""), "eyes": self.appearance.get("eyes", ""), # ... but that requires knowing the keys in advance. Not scalable. Therefore, we decide to use: We'll create a dictionary that has: { "character": { "name": self.name, "age": self.age, "appearance": self.appearance, "personality_traits": self.personality_traits }, **kwargs } Then we tell the writer of the dialogue lines to use the format: "Hello, I'm {character['name']}. I have {character['appearance']['hair']} hair." But that is a bit ugly. Alternatively, we can use an object that allows attribute-style access for dictionaries: class AttributeDict(dict): __getattr__ = dict.__getitem__ __setattr__ = dict.__setitem__ __delattr__ = dict.__delitem__ Then: char_dict = AttributeDict({ "name": self.name, "age": self.age, "appearance": AttributeDict(self.appearance), "personality_traits": self.personality_traits }) Then the line can be: "Hello, I'm {character.name}. I have {character.appearance.hair} hair." And we pass: line.format(character=char_dict, **kwargs) But note: if there are underscores in the keys, they become attributes? And if a key is the same as a built-in attribute? But appearance keys are simple. We can do this. Steps in get_formatted_dialogue_line: char_dict = AttributeDict({ ... }) context_dict = {'character': char_dict} context_dict.update(kwargs) return line.format(**context_dict) But wait, the line uses {} and we are passing a dictionary with keys. The format method will look for the keys in the string. And we are including the 'character' key and any kwargs. However, the line might be: "Hello, I'm {name} and I'm {age} years old." # without the character prefix. Then we would need to have those keys at the top level. But we don't have them at the top level, they are inside character. So we have two options: Option 1: Require that the dialogue lines always use the full path (character.name) to avoid ambiguity and to allow for other context keys (like mood) to be passed without conflict. Option 2: Flatten the character attributes into the top-level context. But then if the character has an attribute 'name', and we also pass a variable 'name' in kwargs, which one takes precedence? We can let kwargs override. We'll go with Option 2 for simplicity and because it's common to refer to the character by simple attributes in the line. We can do: base_dict = { "name": self.name, "age": self.age, **self.appearance, # now we are flattening the appearance -> but if there is a conflict between appearance and base attributes? Then we have a problem. } But note: we control the appearance keys. We can avoid having appearance keys named "name" or "age". Or we can prefix the appearance keys? But that would be cumbersome. Alternatively, we can merge without conflict: if a key in appearance conflicts with a base attribute, we skip? But then we lose data. We decide: we will not include base attributes (name, age) in the appearance. Then we can flatten: base_dict = { "name": self.name, "age": self.age, **self.appearance } Then update with kwargs. Then: line = line.format(**base_dict, **kwargs) But note: what if the line uses a key that is not in base_dict or kwargs? Then it will throw a KeyError. We can ignore missing keys by using a safer formatting: class SafeFormatter(str.Formatter): def get_value(self, key, args, kwargs): if isinstance(key, str): try: return kwargs[key] except KeyError: return "{" + key + "}" # leave the placeholder unchanged else: return super().get_value(key, args, kwargs) Then use: formatter = SafeFormatter() line = formatter.format(line, **base_dict, **kwargs) But that might be overkill. Given time, we'll do: We'll use the base_dict and kwargs and hope the writer provides all keys. Or we can do: base_dict = { "name": self.name, "age": self.age, } # add appearance as individual keys? Only if they are not already in base_dict? # We can do: base_dict.update({k: v for k, v in self.appearance.items() if k not in base_dict}) But then if the writer uses a key that is in base_dict and also in appearance, the base_dict one wins. Alternatively, we can have a way for the writer to specify if they want an appearance attribute by using a prefix? We'll stick to the simple method: flatten the appearance and base attributes, and then update with kwargs. And we assume the appearance keys do not conflict with base attributes (name, age). If they do, the base attributes will be used. We'll document that the base attributes are: name, age. Given the complexity, we'll leave the formatting to be done by the caller? Or we can avoid formatting in the template class and let the app do it. Alternatively, we can design without formatting in the template. We can just return the raw string and then the app is responsible for replacing placeholders. Since the requirement is to create a template for the profile, we'll skip the formatting in the class and let the app handle it. We'll just store the lines as strings. Then the app can use: line = character.get_dialogue_line(context) formatted_line = line.format(name=character.name, age=character.age, **character.appearance, **kwargs) But then the app has to know the structure. We can provide a method that returns a formatting dictionary for the character without kwargs: def get_format_dict(self): return { "name": self.name, "age": self.age, **self.appearance } Then the app can do: base_dict = character.get_format_dict() formatted_line = line.format(**base_dict, **kwargs) This is a good compromise. So we'll add: def get_format_dict(self): base = { "name": self.name, "age": self.age } base.update(self.appearance) return base And then the get_dialogue_line returns a string with placeholders. We can also leave the formatting entirely to the app? Since the app might have more context (like the current mood) that is passed via kwargs. So we'll not do formatting inside the class. Summary of the class: class CharacterProfile: def __init__(self, name, age): self.name = name self.age = age self.appearance = {} self.personality_traits = [] self.dialogue_samples = {} # key: context (str), value: list of lines (str) self.narrator_rules = [] self.writing_style = [] def set_appearance(self, **kwargs): self.appearance.update(kwargs) def add_personality_trait(self, trait): if trait not in self.personality_traits: self.personality_traits.append(trait) def add_dialogue_sample(self, context, lines): if isinstance(lines, str): lines = [lines] if context in self.dialogue_samples: self.dialogue_samples[context].extend(lines) else: self.dialogue_samples[context] = lines def add_narrator_rule(self, rule): self.narrator_rules.append(rule) def add_writing_style_guideline(self, guideline): self.writing_style.append(guideline) def get_dialogue_line(self, context): if context in self.dialogue_samples and self.dialogue_samples[context]: return random.choice(self.dialogue_samples[context]) return None # or we could return a default? but let the app handle missing def get_format_dict(self): base = {"name": self.name, "age": self.age} base.update(self.appearance) return base # ... and to_dict and from_dict as before. Example of creating a profile: alice = CharacterProfile("Alice", 25) alice.set_appearance(hair="blonde", eyes="blue", height="5'6\"") alice.add_personality_trait("kind") alice.add_dialogue_sample("greeting", "Hello, I'm {name}.") alice.add_dialogue_sample("greeting", "Hi, I'm {name}. Nice to meet you!") Then in the app: context = "greeting" line_template = alice.get_dialogue_line(context) if line_template: format_dict = alice.get_format_dict() # and if there are additional context variables, say the other character's name is "Bob" line = line_template.format(**format_dict, other_name="Bob") # then we get: "Hello, I'm Alice." or "Hi, I'm Alice. Nice to meet you!" But note: the line might have {other_name}. So we pass it in the format call. This is modular and scalable. We'll write the code accordingly. We'll also include a way to add multiple traits at once, etc. But those are easy to add later. Now, let's code accordingly. Note: We are using random, so we import it. We'll also add a __repr__ for debugging. Let's write the code. </think>Here's a scalable Python template for creating female character profiles with dialogue systems, designed for modularity and extensibility in a narrative application: ```python import random from dataclasses import dataclass, field from typing import Dict, List, TypedDict, Protocol class DialoguePattern(TypedDict): """Structured dialogue samples with emotional context""" neutral: List[str] happy: List[str] angry: List[str] # Add other emotions as needed class NarrationRule(TypedDict): """Rules for character-specific narration""" action_format: str thought_format: str descriptive_keywords: List[str] class CharacterProfile: def __init__(self, name: str, age: int): self.name = name self.age = age self.appearance: Dict[str, str] = {} self.personality: Dict[str, float] = {} # Trait: Intensity (0.0-1.0) self.dialogue: Dict[str, DialoguePattern] = {} # Context: DialoguePattern self.narration_rules: NarrationRule = { 'action_format': "{character} {action}", 'thought_format': "{character} thought: '{thought}'", 'descriptive_keywords': [] } self.writing_style: Dict[str, str] = { 'sentence_structure': "complex", 'formality': "medium", 'quirk': "" } def add_dialogue_sample(self, context: str, lines: List[str], emotion: str = "neutral") -> None: """Add dialogue samples with emotional context""" if context not in self.dialogue: self.dialogue[context] = { 'neutral': [], 'happy': [], 'angry': [], # Add other emotions } self.dialogue[context][emotion].extend(lines) def add_narration_keyword(self, keyword: str) -> None: """Add descriptive keywords for narration""" self.narration_rules['descriptive_keywords'].append(keyword) def generate_dialogue(self, context: str, emotion: str = "neutral", **kwargs) -> str: """Generate contextual dialogue with dynamic formatting""" if context in self.dialogue and emotion in self.dialogue[context]: template = random.choice(self.dialogue[context][emotion]) return template.format( name=self.name, **self.appearance, **kwargs ) return f"[{context} dialogue missing]" def generate_action(self, verb: str) -> str: """Generate character action narration""" keyword = random.choice(self.narration_rules['descriptive_keywords']) if self.narration_rules['descriptive_keywords'] else "" return self.narration_rules['action_format'].format( character=self.name, action=f"{keyword} {verb}" if keyword else verb ) def generate_thought(self, content: str) -> str: """Generate character thought narration""" return self.narration_rules['thought_format'].format( character=self.name, thought=content ) class CharacterFactory: @staticmethod def create_template(name: str, age: int) -> CharacterProfile: """Factory method for standardized character creation""" character = CharacterProfile(name, age) # Default dialogue samples character.add_dialogue_sample("greeting", [ "Hello, I'm {name}. {context_comment}", "Hi there! These {hair_color} strands? Yeah, they're natural." ]) # Default narration rules character.add_narration_keyword("gracefully") character.add_narration_keyword("with a thoughtful expression") # Default writing style character.writing_style.update({ 'contraction_usage': "high", 'pause_pattern': "...", 'regional_dialect': "" }) return character # Example Usage if __name__ == "__main__": # Create character elara = CharacterFactory.create_template("Elara Vance", 28) # Customize attributes elara.appearance = { "eye_color": "emerald green", "hair_color": "raven black", "height": "5'8\"" } # Add personality traits with intensity elara.personality = { "witty": 0.9, "compassionate": 0.8, "stubborn": 0.7 } # Add context-specific dialogue elara.add_dialogue_sample("mission_briefing", [ "Understood. But {secondary_character}, have you considered the {obstacle}?", "This plan has {probability}% chance of success. I recommend {alternative_plan}." ], "neutral") # Generate output print("Generated Dialogue:") print(elara.generate_dialogue("greeting", context_comment="Ready for the briefing?")) print("\nGenerated Action:") print(elara.generate_action("examined the holographic map")) print("\nGenerated Thought:") print(elara.generate_thought("This plan has more holes than Swiss cheese")) ``` ### Key Components Explanation: 1. **CharacterProfile Class**: - Central data store for character attributes - Modular sections for appearance, personality, dialogue, narration rules, and writing style - Methods for generating context-appropriate dialogue and narration 2. **Dialogue System**: - Emotionally-tagged dialogue patterns (neutral/happy/angry) - Contextual templates with placeholder formatting - Dynamic variable injection in dialogue strings 3. **Narration Engine**: - Configurable action/thought formatting - Descriptive keyword system for character-specific adverbs - Separation of narration logic from core character data 4. **CharacterFactory**: - Standardized template creation - Pre-configured dialogue samples and narration rules - Ensures consistent baseline for all characters 5. **Writing Style Configuration**: - Linguistic attributes (sentence structure, formality) - Speech patterns (contractions, pauses, dialect) - Expandable quirk system for unique speech characteristics ### Scalability Features: 1. **Emotion Matrix**: Expand by adding new emotion keys (sad/sarcastic/flirty) 2. **Context Inheritance**: Implement fallback dialogue contexts for missing scenarios 3. **Trait System**: Personality traits could influence dialogue selection probabilities 4. **Localization**: Add locale-specific writing style profiles 5. **Version Control**: Serialization/deserialization support for profile saving ### Example Output: ``` Generated Dialogue: Hello, I'm Elara Vance. Ready for the briefing? Generated Action: Elara Vance thoughtfully examined the holographic map Generated Thought: Elara Vance thought: 'This plan has more holes than Swiss cheese' ``` This template provides a foundation for: - Character consistency across scenes - Dynamic dialogue generation - Narration personalization - Style-consistent writing - Easy expansion via new dialogue contexts and emotions
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