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from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
from langchain.chains import LLMChain
from langchain.chains import SequentialChain
from langchain.llms import HuggingFaceHub
from dotenv import load_dotenv
# load_dotenv();
config = {'max_new_tokens': 512, 'temperature': 0.6}
# Create function for app
def GetLLMResponse(selected_topic_level,
selected_topic,
num_quizzes):
# Calling llama model
# llm = CTransformers(model="D:\Code Workspace\DL Model\llama-2-7b-chat.ggmlv3.q8_0.bin",
# model_type = 'llama',
# config = config)
# llm = CTransformers(model='TheBloke/Llama-2-7B-Chat-GGML',
# model_file = 'llama-2-7b-chat.ggmlv3.q8_0.bin',
# model_type = 'llama',
# config = config)
llm = HuggingFaceHub(
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs = config
)
## Create LLM Chaining
questions_template = "I want you to just generate question with this specification: Generate a {selected_topic_level} math quiz on the topic of {selected_topic}. Generate only {num_quizzes} questions not more and without providing answers. The Question should not in image format/link"
questions_prompt = PromptTemplate(input_variables=["selected_topic_level", "selected_topic", "num_quizzes"],
template=questions_template)
questions_chain = LLMChain(llm= llm,
prompt = questions_prompt,
output_key = "questions")
answer_template = "I want you to become a teacher answer this specific Question:\n {questions}\n\n. You should gave me a straightforward and consise explanation and answer to each one of them"
answer_prompt = PromptTemplate(input_variables = ["questions"],
template = answer_template)
answer_chain = LLMChain(llm = llm,
prompt = answer_prompt,
output_key = "answer")
## Create Sequential Chaining
seq_chain = SequentialChain(chains = [questions_chain, answer_chain],
input_variables = ['selected_topic_level', 'selected_topic', 'num_quizzes'],
output_variables = ['questions', 'answer'])
response = seq_chain({'selected_topic_level': selected_topic_level,
'selected_topic': selected_topic,
'num_quizzes' : num_quizzes})
## Generate the response from the llama 2 model
print(response)
return response |