app.py

import streamlit as st
import bm25s
from operator import itemgetter
import os
import re
import pandas as pd
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.docstore.document import Document

@st.cache_data
def load_data():
    df = pd.read_csv("cleaned_list.csv",header = None)
    df.columns = ['document']
    corpus = [doc for doc in df['document'].to_list()]

    retriever = bm25s.BM25(corpus=corpus)
    retriever.index(bm25s.tokenize(corpus))

    return retriever

# def extract_hscode(text):
#     match = re.search(r'hs_code:\s*(\d+)', text)
#     if match:
#         return match.group(1)
#     return None

# df2 = pd.read_csv("hscode_main.csv")
# new_col = [len(str(code))for code in df2['hs_code'].to_list()]
# df2['len'] = new_col

# new_hscode = [str(code) for code in df2['hs_code']]

# for i in range(len(new_col)):
#     if new_col[i]==5:
#         new_hscode[i] = '0'+ new_hscode[i]
# df2['hs_code'] = new_hscode
# df2=df2.drop(columns='len')

# if 'retriever' not in st.session_state:
#     st.session_state.retriever = None

# if st.session_state.retriever is None:
#     st.session_state.retriever = load_data()


# sentence = st.text_input("please enter description:")

# if sentence !='':
#     results,_ = st.session_state.retriever.retrieve(bm25s.tokenize(sentence), k=5)
#     doc = [d for d in results]
#     hscodes = [extract_hscode(item) for item in doc[0]]
#     for code in hscodes:
#         if len(code)==5:
#             code = '0'+ code

#         filter_df = df2[df2['hs_code']==code]
#         answer = filter_df['description'].iloc[0]
#         st.write("Hscode:",code)
#         st.write("Description:",answer.lower())

def load_model():
    prompt = ChatPromptTemplate.from_messages([
        HumanMessagePromptTemplate.from_template(
        f"""
        Extract the appropriate 6-digit HS Code base on the product description and retrieved document by thoroughly analyzing its details and utilizing a reliable and up-to-date HS Code database for accurate results.
        Only return the HS Code as a 6-digit number .
        Example: 123456
        Context: {{context}}
        Description: {{description}}
        Answer:
        """
        )
    ])
    

    #device = "cuda" if torch.cuda.is_available() else "cpu"
    
    #llm = OllamaLLM(model="gemma2", temperature=0, device=device)
    #api_key = "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"
    api_key = "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"
    llm = ChatGroq(model = "llama-3.1-70b-versatile", temperature = 0,api_key = api_key)
    chain = prompt|llm
    return chain

def process_input(sentence):
    docs, _ = st.session_state.retriever.retrieve(bm25s.tokenize(sentence), k=15)
    documents =[]
    for doc in docs[0]:
        documents.append(Document(doc)) 
    return documents
    
if 'retriever' not in st.session_state:
    st.session_state.retriever = None

if 'chain' not in st.session_state:
    st.session_state.chain = None
    
if st.session_state.retriever is None:
    st.session_state.retriever = load_data()

if st.session_state.chain is None:
    st.session_state.chain = load_model()
    
sentence = st.text_input("please enter description:")

if sentence !='':
    documents = process_input(sentence)
    hscode = st.session_state.chain.invoke({'context': documents,'description':sentence})
    st.write("answer:",hscode.content)