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import streamlit as st
import pandas as pd
import os
import tempfile
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import faiss
import openai
# OpenAI API key configuration
st.set_page_config(page_title="RAG Chatbot with Files", layout="centered")
openai.api_key = st.sidebar.text_input("Enter OpenAI API Key:", type="password")
# Initialize FAISS and embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
faiss_index = None
data_chunks = []
chunk_mapping = {}
# File Upload and Processing
def load_files(uploaded_files):
global data_chunks, chunk_mapping, faiss_index
data_chunks = []
chunk_mapping = {}
for uploaded_file in uploaded_files:
file_type = uploaded_file.name.split('.')[-1]
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_file_path = tmp_file.name
if file_type == "csv":
df = pd.read_csv(tmp_file_path)
content = "\n".join(df.astype(str).values.flatten())
elif file_type == "xlsx":
df = pd.read_excel(tmp_file_path)
content = "\n".join(df.astype(str).values.flatten())
elif file_type == "pdf":
reader = PdfReader(tmp_file_path)
content = "".join([page.extract_text() for page in reader.pages])
else:
st.error(f"Unsupported file type: {file_type}")
continue
# Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_text(content)
data_chunks.extend(chunks)
chunk_mapping.update({i: (uploaded_file.name, chunk) for i, chunk in enumerate(chunks)})
# Create FAISS index
embeddings = embedding_model.encode(data_chunks)
faiss_index = faiss.IndexFlatL2(embeddings.shape[1])
faiss_index.add(embeddings)
# Query Processing
def handle_query(query):
if not faiss_index:
return "No data available. Please upload files first."
# Generate embedding for the query
query_embedding = embedding_model.encode([query])
distances, indices = faiss_index.search(query_embedding, k=5)
relevant_chunks = [chunk_mapping[idx][1] for idx in indices[0]]
# Use OpenAI for summarization
prompt = "Summarize the following information:\n" + "\n".join(relevant_chunks)
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=150
)
return response['choices'][0]['text']
# Streamlit UI
def main():
st.title("RAG Chatbot with Files")
st.sidebar.title("Options")
uploaded_files = st.sidebar.file_uploader("Upload files (CSV, Excel, PDF):", type=["csv", "xlsx", "pdf"], accept_multiple_files=True)
if uploaded_files:
load_files(uploaded_files)
st.sidebar.success("Files loaded successfully!")
query = st.text_input("Ask a question about the data:")
if st.button("Get Answer"):
if openai.api_key and query:
answer = handle_query(query)
st.subheader("Answer:")
st.write(answer)
else:
st.error("Please provide a valid API key and query.")
if __name__ == "__main__":
main()
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