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import streamlit as st
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import LlamaCpp
from llama_cpp import Llama
from pinecone import Pinecone
from huggingface_hub import hf_hub_download
@st.cache_resource()
def load_model():
# from google.colab import userdata
model_name_or_path = "CompendiumLabs/bge-large-en-v1.5-gguf"
model_basename = 'bge-large-en-v1.5-f32.gguf'
model_path = hf_hub_download(
repo_id=model_name_or_path,
filename=model_basename,
cache_dir= '/content/models' # Directory for the model
)
model = Llama(model_path, embedding=True)
st.success("Loaded NLP model from Hugging Face!") # 👈 Show a success message
model_2_name = "TheBloke/zephyr-7B-beta-GGUF"
model_2base_name = "zephyr-7b-beta.Q4_K_M.gguf"
model_path_model = hf_hub_download(
repo_id=model_2_name,
filename=model_2base_name,
cache_dir= '/content/models' # Directory for the model
)
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
model_path=model_path_model,
temperature=0.75,
max_tokens=2500,
top_p=1,
callback_manager=callback_manager,
verbose=True,
n_ctx=2048,
n_threads = 2# Verbose is required to pass to the callback manager
)
st.success("loaded the second NLP model from Hugging Face!")
# prompt_template = "<|system|>\
# </s>\
# <|user|>\
# {prompt}</s>\
# <|assistant|>"
# template = prompt_template
# prompt = PromptTemplate.from_template(template)
return model, llm
st.title("Please ask your question on Lithuanian rules for foreigners.")
model,llm = load_model()
pc = Pinecone(api_key=apikeys)
index = pc.Index("law")
question = st.text_input("Enter your question:")
query = model.create_embedding(question)
q = query['data'][0]['embedding']
response = index.query(
vector=q,
top_k=1,
include_metadata = True,
namespace = "ns1"
)
response_t = response['matches'][0]['metadata']['text']
st.header("Answer:")
st.write(response_t) |