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import streamlit as st
import os
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import ConversationalRetrievalChain
def add_vertical_space(spaces=1):
for _ in range(spaces):
st.sidebar.markdown("---")
def main():
st.set_page_config(page_title="Llama-2-GGML CSV Chatbot")
st.title("Llama-2-GGML CSV Chatbot")
st.sidebar.title("About")
st.sidebar.markdown('''
The Llama-2-GGML CSV Chatbot uses the **Llama-2-7B-Chat-GGML** model.
### 🔄Bot evolving, stay tuned!
## Useful Links 🔗
- **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main) 📚
- **GitHub:** [ThisIs-Developer/Llama-2-GGML-CSV-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot) 💬
''')
DB_FAISS_PATH = "vectorstore/db_faiss"
TEMP_DIR = "temp"
if not os.path.exists(TEMP_DIR):
os.makedirs(TEMP_DIR)
uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'])
add_vertical_space(1)
st.sidebar.write('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
if uploaded_file is not None:
file_path = os.path.join(TEMP_DIR, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getvalue())
st.write(f"Uploaded file: {uploaded_file.name}")
st.write("Processing CSV file...")
loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
text_chunks = text_splitter.split_documents(data)
st.write(f"Total text chunks: {len(text_chunks)}")
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
docsearch = FAISS.from_documents(text_chunks, embeddings)
docsearch.save_local(DB_FAISS_PATH)
llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
model_type="llama",
max_new_tokens=512,
temperature=0.1)
qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
st.write("Enter your query:")
query = st.text_input("Input Prompt:")
if query:
with st.spinner("Processing your question..."):
chat_history = []
result = qa({"question": query, "chat_history": chat_history})
st.write("Response:", result['answer'])
os.remove(file_path)
if __name__ == "__main__":
main()
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