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from abc import ABC, abstractmethod
from typing import Type, TypeVar
import base64
import json
# constants
image_embed_prefix = "🖼️🆙 "
log_to_console = False
def encode_image(image_data):
"""Generates a prefix for image base64 data in the required format for the
four known image formats: png, jpeg, gif, and webp.
Args:
image_data: The image data, encoded in base64.
Returns:
An object encoding the image
"""
# Get the first few bytes of the image data.
magic_number = image_data[:4]
# Check the magic number to determine the image type.
if magic_number.startswith(b'\x89PNG'):
image_type = 'png'
elif magic_number.startswith(b'\xFF\xD8'):
image_type = 'jpeg'
elif magic_number.startswith(b'GIF89a'):
image_type = 'gif'
elif magic_number.startswith(b'RIFF'):
if image_data[8:12] == b'WEBP':
image_type = 'webp'
else:
# Unknown image type.
raise Exception("Unknown image type")
else:
# Unknown image type.
raise Exception("Unknown image type")
return {"type": "base64",
"media_type": "image/" + image_type,
"data": base64.b64encode(image_data).decode('utf-8')}
LLMClass = TypeVar('LLMClass', bound='LLM')
class LLM(ABC):
@abstractmethod
def generate_body(message, history, system_prompt, temperature, max_tokens):
pass
@abstractmethod
def read_response(message, history, system_prompt, temperature, max_tokens):
pass
@staticmethod
def create_llm(model: str) -> Type[LLMClass]:
if model.startswith("anthropic.claude"):
return Claude()
elif model.startswith("mistral."):
return Mistral()
else:
raise ValueError(f"Unsupported model: {model}")
class Claude(LLM):
@staticmethod
def generate_body(message, history, system_prompt, temperature, max_tokens):
history_claude_format = []
user_msg_parts = []
for human, assi in history:
if human is not None:
if human.startswith(image_embed_prefix):
with open(human.lstrip(image_embed_prefix), mode="rb") as f:
content = f.read()
user_msg_parts.append({"type": "image",
"source": encode_image(content)})
else:
user_msg_parts.append({"type": "text", "text": human})
if assi is not None:
if user_msg_parts:
history_claude_format.append({"role": "user", "content": user_msg_parts})
user_msg_parts = []
history_claude_format.append({"role": "assistant", "content": assi})
if message:
user_msg_parts.append({"type": "text", "text": human})
if user_msg_parts:
history_claude_format.append({"role": "user", "content": user_msg_parts})
if log_to_console:
print(f"br_prompt: {str(history_claude_format)}")
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"system": system_prompt,
"max_tokens": max_tokens,
"temperature": temperature,
"messages": history_claude_format
})
return body
@staticmethod
def read_response(response_body) -> Type[str]:
return response_body.get('content')[0].get('text')
class Mistral(LLM):
@staticmethod
def generate_body(message, history, system_prompt, temperature, max_tokens):
prompt = "<s>"
for human, assi in history:
if prompt is not None:
prompt += f"[INST] {human} [/INST]\n"
if assi is not None:
prompt += f"{assi}</s>\n"
if message:
prompt += f"[INST] {message} [/INST]"
if log_to_console:
print(f"br_prompt: {str(prompt)}")
body = json.dumps({
"prompt": prompt,
"max_tokens": max_tokens,
"temperature": temperature,
})
return body
@staticmethod
def read_response(response_body) -> Type[str]:
return response_body.get('outputs')[0].get('text')
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