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import streamlit as st
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
# DB_FAISS_PATH = 'vectorstores/db_faiss/NE-Syllabus'
# custom_prompt_template = """Use the following pieces of information to answer the user's question.
# If you don't know the answer, just say that you don't know, don't try to make up an answer.
# # Context: {answer}
# # Question: {question}
# Only return the helpful answer below and nothing else.
# Helpful answer:
# """
# def set_custom_prompt():
# prompt = PromptTemplate(template=custom_prompt_template,
# input_variables=['context', 'question'])
# return prompt
# def retrieval_qa_chain(llm, prompt, db):
# qa_chain = RetrievalQA.from_chain_type(llm=llm,
# chain_type='stuff',
# retriever=db.as_retriever(
# search_kwargs={'k': 2}),
# return_source_documents=True,
# chain_type_kwargs={'prompt': prompt}
# )
# return qa_chain
# def load_llm():
# llm = CTransformers(
# model="TheBloke/Llama-2-7B-Chat-GGML",
# model_type="llama",
# max_new_tokens=512,
# temperature=0.5
# )
# return llm
# def qa_bot(query):
# # sentence-transformers/all-MiniLM-L6-v2
# embeddings = HuggingFaceEmbeddings(model_name="imdeadinside410/TestTrainedModel",
# model_kwargs={'device': 'cpu'})
# db = FAISS.load_local(DB_FAISS_PATH, embeddings)
# llm = load_llm()
# qa_prompt = set_custom_prompt()
# qa = retrieval_qa_chain(llm, qa_prompt, db)
# # Implement the question-answering logic here
# response = qa({'query': query})
# return response['result']
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import pipeline
from langchain.prompts import PromptTemplate
import torch
from torch import cuda
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "imdeadinside410/Llama2-Syllabus"
# device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path, return_dict=True, load_in_4bit=True, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
pipe = pipeline(task="text-generation",
model=model,
tokenizer=tokenizer, max_length=300)
# result = pipe(f"<s>[INST] {prompt} [/INST]")
# print(result[0]['generated_text'].split("[/INST]")[1])
# template = """Question: {question}
# Answer: Let's think step by step."""
# prompt = PromptTemplate.from_template(template)
# chain = prompt | hf
# question = "What is IT ?"
# print(chain.invoke({"question": question}))
def add_vertical_space(spaces=1):
for _ in range(spaces):
st.markdown("---")
def main():
st.set_page_config(page_title="AIoTLab NE Syllabus")
with st.sidebar:
st.title('AIoTLab NE Syllabus')
st.markdown('''
Hi
''')
add_vertical_space(1) # Adjust the number of spaces as needed
st.write(
'AIoT Lab')
st.title("AIoTLab NE Syllabus")
st.markdown(
"""
<style>
.chat-container {
display: flex;
flex-direction: column;
height: 400px;
overflow-y: auto;
padding: 10px;
color: white; /* Font color */
}
.user-bubble {
background-color: #007bff; /* Blue color for user */
align-self: flex-end;
border-radius: 10px;
padding: 8px;
margin: 5px;
max-width: 70%;
word-wrap: break-word;
}
.bot-bubble {
background-color: #363636; /* Slightly lighter background color */
align-self: flex-start;
border-radius: 10px;
padding: 8px;
margin: 5px;
max-width: 70%;
word-wrap: break-word;
}
</style>
""", unsafe_allow_html=True)
conversation = st.session_state.get("conversation", [])
query = st.text_input("Please input your question here:", key="user_input")
result = pipe(f"<s>[INST] {prompt} [/INST]")
if st.button("Get Answer"):
if query:
# Display the processing message
with st.spinner("Processing your question..."):
conversation.append({"role": "user", "message": query})
# Call your QA function
answer = result[0]['generated_text'].split("[/INST]")[1]
conversation.append({"role": "bot", "message": answer})
st.session_state.conversation = conversation
else:
st.warning("Please input a question.")
chat_container = st.empty()
chat_bubbles = ''.join(
[f'<div class="{c["role"]}-bubble">{c["message"]}</div>' for c in conversation])
chat_container.markdown(
f'<div class="chat-container">{chat_bubbles}</div>', unsafe_allow_html=True)
if __name__ == "__main__":
main()
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