Kye Gomez
3 weeks ago
parent
22758f222c
commit
dc891a8cac
@ -1,85 +0,0 @@
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import json
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from typing import List
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class PromptGenerator:
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"""A class for generating custom prompt strings."""
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def __init__(self) -> None:
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"""Initialize the PromptGenerator object."""
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self.constraints: List[str] = []
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self.commands: List[str] = []
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self.resources: List[str] = []
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self.performance_evaluation: List[str] = []
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self.response_format = {
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"thoughts": {
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"text": "thought",
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"reasoning": "reasoning",
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"plan": (
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"- short bulleted\n- list that conveys\n-"
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" long-term plan"
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),
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"criticism": "constructive self-criticism",
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"speak": "thoughts summary to say to user",
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},
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"command": {
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"name": "command name",
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"args": {"arg name": "value"},
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},
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}
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def add_constraint(self, constraint: str) -> None:
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"""
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Add a constraint to the constraints list.
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Args:
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constraint (str): The constraint to be added.
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"""
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self.constraints.append(constraint)
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def add_command(self, command: str) -> None:
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"""
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Add a command to the commands list.
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Args:
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command (str): The command to be added.
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"""
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self.commands.append(command)
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def add_resource(self, resource: str) -> None:
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"""
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Add a resource to the resources list.
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Args:
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resource (str): The resource to be added.
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"""
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self.resources.append(resource)
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def add_performance_evaluation(self, evaluation: str) -> None:
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"""
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Add a performance evaluation item to the performance_evaluation list.
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Args:
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evaluation (str): The evaluation item to be added.
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"""
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self.performance_evaluation.append(evaluation)
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def generate_prompt_string(self) -> str:
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"""Generate a prompt string.
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Returns:
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str: The generated prompt string.
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"""
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formatted_response_format = json.dumps(
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self.response_format, indent=4
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)
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prompt_string = (
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f"Constraints:\n{''.join(self.constraints)}\n\nCommands:\n{''.join(self.commands)}\n\nResources:\n{''.join(self.resources)}\n\nPerformance"
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f" Evaluation:\n{''.join(self.performance_evaluation)}\n\nYou"
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" should only respond in JSON format as described below"
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" \nResponse Format:"
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f" \n{formatted_response_format} \nEnsure the response"
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" can be parsed by Python json.loads"
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)
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return prompt_string
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@ -1,159 +0,0 @@
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from __future__ import annotations
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from abc import abstractmethod
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from typing import Sequence
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class Message:
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"""
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The base abstract Message class.
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Messages are the inputs and outputs of ChatModels.
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"""
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def __init__(
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self, content: str, role: str, additional_kwargs: dict = None
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):
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self.content = content
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self.role = role
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self.additional_kwargs = (
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additional_kwargs if additional_kwargs else {}
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)
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@abstractmethod
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def get_type(self) -> str:
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pass
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class HumanMessage(Message):
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"""
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A Message from a human.
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"""
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def __init__(
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self,
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content: str,
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role: str = "Human",
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additional_kwargs: dict = None,
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example: bool = False,
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):
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super().__init__(content, role, additional_kwargs)
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self.example = example
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def get_type(self) -> str:
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return "human"
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class AIMessage(Message):
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"""
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A Message from an AI.
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"""
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def __init__(
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self,
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content: str,
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role: str = "AI",
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additional_kwargs: dict = None,
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example: bool = False,
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):
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super().__init__(content, role, additional_kwargs)
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self.example = example
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def get_type(self) -> str:
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return "ai"
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class SystemMessage(Message):
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"""
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A Message for priming AI behavior, usually passed in as the first of a sequence
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of input messages.
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"""
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def __init__(
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self,
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content: str,
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role: str = "System",
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additional_kwargs: dict = None,
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):
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super().__init__(content, role, additional_kwargs)
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def get_type(self) -> str:
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return "system"
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class FunctionMessage(Message):
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"""
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A Message for passing the result of executing a function back to a model.
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"""
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def __init__(
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self,
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content: str,
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role: str = "Function",
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name: str = None,
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additional_kwargs: dict = None,
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):
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super().__init__(content, role, additional_kwargs)
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self.name = name
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def get_type(self) -> str:
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return "function"
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class ChatMessage(Message):
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"""
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A Message that can be assigned an arbitrary speaker (i.e. role).
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"""
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def __init__(
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self, content: str, role: str, additional_kwargs: dict = None
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):
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super().__init__(content, role, additional_kwargs)
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def get_type(self) -> str:
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return "chat"
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def get_buffer_string(
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messages: Sequence[Message],
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human_prefix: str = "Human",
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ai_prefix: str = "AI",
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) -> str:
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string_messages = []
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for m in messages:
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message = f"{m.role}: {m.content}"
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if (
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isinstance(m, AIMessage)
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and "function_call" in m.additional_kwargs
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):
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message += f"{m.additional_kwargs['function_call']}"
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string_messages.append(message)
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return "\n".join(string_messages)
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def message_to_dict(message: Message) -> dict:
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return {"type": message.get_type(), "data": message.__dict__}
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def messages_to_dict(messages: Sequence[Message]) -> list[dict]:
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return [message_to_dict(m) for m in messages]
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def message_from_dict(message: dict) -> Message:
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_type = message["type"]
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if _type == "human":
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return HumanMessage(**message["data"])
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elif _type == "ai":
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return AIMessage(**message["data"])
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elif _type == "system":
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return SystemMessage(**message["data"])
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elif _type == "chat":
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return ChatMessage(**message["data"])
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elif _type == "function":
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return FunctionMessage(**message["data"])
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else:
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raise ValueError(f"Got unexpected message type: {_type}")
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def messages_from_dict(messages: list[dict]) -> list[Message]:
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return [message_from_dict(m) for m in messages]
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@ -1,19 +1,14 @@
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IMAGE_ENRICHMENT_PROMPT = (
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"Create a concise and effective image generation prompt within"
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" 400 characters or less, "
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"based on Stable Diffusion and Dalle best practices. Starting"
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" prompt: \n\n'"
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# f"{prompt}'\n\n"
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"Improve the prompt with any applicable details or keywords by"
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" considering the following aspects: \n"
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"1. Subject details (like actions, emotions, environment) \n"
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"2. Artistic style (such as surrealism, hyperrealism) \n"
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"3. Medium (digital painting, oil on canvas) \n"
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"4. Color themes and lighting (like warm colors, cinematic"
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" lighting) \n"
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"5. Composition and framing (close-up, wide-angle) \n"
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"6. Additional elements (like a specific type of background,"
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" weather conditions) \n"
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"7. Any other artistic or thematic details that can make the"
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" image more vivid and compelling."
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)
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IMAGE_ENRICHMENT_PROMPT = """
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Create a concise and effective image generation prompt within 400 characters or less, based on Stable Diffusion and Dalle best practices.
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Improve the prompt with any applicable details or keywords by considering the following aspects:
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1. Subject details (like actions, emotions, environment)
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2. Artistic style (such as surrealism, hyperrealism)
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3. Medium (digital painting, oil on canvas)
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4. Color themes and lighting (like warm colors, cinematic lighting)
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5. Composition and framing (close-up, wide-angle)
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6. Additional elements (like a specific type of background, weather conditions)
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7. Any other artistic or thematic details that can make the image more vivid and compelling.
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"""
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