app.py

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,
)
    model = Llama(model_path, embedding=True)

    st.success("Loaded NLP model from Hugging Face!")  # 👈 Show a success message
    apikey = st.secrets["apikey"]
    pc = Pinecone(api_key=apikey)
    index = pc.Index("law")

    # pc = Pinecone(api_key=api_key)
    # index = pc.Index("law")
    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,
)
    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, index


# st.title("Please ask your question on Lithuanian rules for foreigners.")
model,llm, index  = load_model()

# question = st.text_input("Enter your question:")

# if question != "":
#     query = model.create_embedding(question)
#     st.write(query)
#     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.write(response_t)
st.header("Answer:")
def handle_question():
    question = st.text_input("Enter your question:")
    if question != "":
        # ... (Your query and response logic)
        query = model.create_embedding(question)
        # st.write(query)
        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.write(response_t)

# Use a button to trigger handling of the question 
if st.button("Submit Question"):
    handle_question()