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59 lines
2.0 KiB
59 lines
2.0 KiB
3 months ago
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"""
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This file is designed to invoke Wenxinyi GPT under Baidu QianFan integration
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to generate possible prompt words for specifying manim code.
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---
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To use it, add the corresponding Key in API_key.cfg'''QIANFAN_AK/QIANFAN_SK'''
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External file:./API_Key.cfg
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Depends:pip install qianfan>=0.3.11
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"""
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import os
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import configparser
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from langchain_community.chat_models import QianfanChatEndpoint
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from langchain_core.language_models.chat_models import HumanMessage
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from langchain_core.prompts import ChatPromptTemplate
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ABS_PATH = os.path.dirname(os.path.abspath(__file__))
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assert os.path.exists(
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f"{ABS_PATH}/API_Key.cfg"
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), f"can not find the {ABS_PATH}/API_Key.cfg"
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# Load Setttings
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CFG_API_KEY = configparser.ConfigParser()
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CFG_API_KEY.read(f"{ABS_PATH}/API_Key.cfg", encoding="utf-8")
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os.environ["QIANFAN_AK"] = str(CFG_API_KEY["qianfan"]["QIANFAN_AK"])
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os.environ["QIANFAN_SK"] = str(CFG_API_KEY["qianfan"]["QIANFAN_SK"])
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print(os.environ["QIANFAN_AK"], os.environ["QIANFAN_SK"])
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# Chat stream, integrated with LangChain
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chat_QianFan = QianfanChatEndpoint(
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streaming=False,
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model="ERNIE-3.5-8K", # Well, this one is free
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)
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# input and output
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"If you are a user who is going to use GPT to generate manim code, write the prompt words used by the role according to the code I gave you, write in the same paragraph, do not need to step by step analysis, do not explain your output in the first paragraph:",
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),
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("user", "{input}"),
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]
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)
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chain = prompt | chat_QianFan
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reply = chain.invoke(
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{
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"input": "from manim import * class MyScene(Scene): def construct(self): circle = Circle(radius=2, color=BLUE) self.add(circle)"
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}
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)
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print(reply.content)
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"""
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e.g output
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Generate the Manim code for me that creates a scene with a blue circle of radius 2 centered at the origin. The circle should appear gradually on the screen from left to right. Include comments in the code to explain each step.
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"""
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