app.py

import streamlit as st
from streamlit_chat import message
import tempfile
from langchain_openai import ChatOpenAI
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import ConversationalRetrievalChain
from dl_hf_model import dl_hf_model
from ctransformers import AutoModelForCausalLM
from langchain_g4f import G4FLLM
from g4f import Provider, models
import requests
# Define the path for generated embeddings
DB_FAISS_PATH = 'vectorstore/db_faiss'

# Load the model of choice
def load_llm():
    # url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin"  # 2.87G
    # url ="https://huggingface.co/skeskinen/llama-lite-134m/blob/main/pytorch_model.bin"
    # model_loc, file_size = dl_hf_model(url)
    
    # llm = CTransformers(
    #     model=model_loc,
    #     temperature=0.2,
    #     model_type="llama",
    #     top_k=10,
    #     top_p=0.9,
    #     repetition_penalty=1.0,
    #     max_new_tokens=512,  # adjust as needed
    #     seed=42,
    #     reset=True,  # reset history (cache)
    #     stream=False,
    #     # threads=cpu_count,
    #     # stop=prompt_prefix[1:2],
               
        
    # )
    # llm = G4FLLM(
    #     model=models.gpt_35_turbo,
    #     provider=Provider.Aichatos,
    # )
    OPENAI_API_KEY = 'sk-proj-0eSbY1eTGDvUWXjKwUYeT3BlbkFJ7zG1D208fWZlYi0ByZwa'
    llm =ChatOpenAI(openai_api_key=OPENAI_API_KEY,temperature=0)
    return llm
hide_streamlit_style = """
        <style>
        #MainMenu {visibility: hidden;}
        footer {visibility: hidden;}
        </style>
        """
st.markdown(hide_streamlit_style, unsafe_allow_html=True) 

# Set the title for the Streamlit app
st.title("Coloring Anime ChatBot")

csv_url = "https://huggingface.co/spaces/uyen13/chatbot/raw/main/testchatdata.csv"
# csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE"

# Define the path where you want to save the downloaded file
tmp_file_path = "testchatdata.csv"

# Download the CSV file
response = requests.get(csv_url)
if response.status_code == 200:
    with open(tmp_file_path, 'wb') as file:
        file.write(response.content)
else:
    raise Exception(f"Failed to download the CSV file from {csv_url}")

# Load CSV data using CSVLoader
loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
data = loader.load()

# Create embeddings using Sentence Transformers
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})

# Create a FAISS vector store and save embeddings
db = FAISS.from_documents(data, embeddings)
# db.save_local(DB_FAISS_PATH)


# Load the language model
llm = load_llm()

# Create a conversational chain
chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())

# Function for conversational chat
def conversational_chat(query):
    query ="based on the provided data,"+query
    result = chain({"question": query, "chat_history": st.session_state['history']})
    st.session_state['history'].append((query, result["answer"]))
    return result["answer"]

# Initialize chat history
if 'history' not in st.session_state:
    st.session_state['history'] = []

# Initialize messages
if 'generated' not in st.session_state:
    st.session_state['generated'] = ["Hello ! Ask me about this page like coloring book,how to buy ... 🤗"]

if 'past' not in st.session_state:
    st.session_state['past'] = ["your chat here"]

# Create containers for chat history and user input
response_container = st.container()
container = st.container()

# User input form
with container:
    with st.form(key='my_form', clear_on_submit=True):
        user_input = st.text_input("ChatBox", placeholder="Ask anything...  ", key='input')
        submit_button = st.form_submit_button(label='Send')

    if submit_button and user_input:
        output = conversational_chat(user_input)
        st.session_state['past'].append(user_input)
        st.session_state['generated'].append(output)

# Display chat history
if st.session_state['generated']:
    with response_container:
        for i in range(len(st.session_state['generated'])):
            message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
            message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")