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import streamlit as st
import torch
import os
import time
from threading import Thread
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from langchain_community.document_loaders import PyPDFLoader, TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document
# --- HF Token ---
HF_TOKEN = st.secrets["HF_TOKEN"]
# --- Page Config ---
st.set_page_config(page_title="DigiTwin RAG", page_icon="📂", layout="centered")
st.title("📂 DigiTwin RAG Chat (GM Qwen 1.8B)")
# --- Upload Files Sidebar ---
with st.sidebar:
st.header("📄 Upload Knowledge Files")
uploaded_files = st.file_uploader("Upload PDFs or .txt files", accept_multiple_files=True, type=["pdf", "txt"])
if uploaded_files:
st.success(f"{len(uploaded_files)} file(s) uploaded")
# --- Model Loading ---
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained("amiguel/GM_Qwen1.8B_Finetune", trust_remote_code=True, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
"amiguel/GM_Qwen1.8B_Finetune",
device_map="auto",
torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32,
trust_remote_code=True,
token=HF_TOKEN
)
return model, tokenizer
model, tokenizer = load_model()
# --- Prompt Helper ---
SYSTEM_PROMPT = (
"You are DigiTwin, an expert advisor in asset integrity and reliability engineering. "
"Use the provided context from uploaded documents to answer precisely and professionally."
)
def build_prompt(messages, context=""):
prompt = f"<|im_start|>system\n{SYSTEM_PROMPT}\n\nContext:\n{context}<|im_end|>\n"
for msg in messages:
role = msg["role"]
prompt += f"<|im_start|>{role}\n{msg['content']}<|im_end|>\n"
prompt += "<|im_start|>assistant\n"
return prompt
# --- RAG Embedding and Search ---
@st.cache_resource
def embed_uploaded_files(files):
raw_docs = []
for f in files:
file_path = f"/tmp/{f.name}"
with open(file_path, "wb") as out_file:
out_file.write(f.read())
loader = PyPDFLoader(file_path) if f.name.endswith(".pdf") else TextLoader(file_path)
raw_docs.extend(loader.load())
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64)
chunks = splitter.split_documents(raw_docs)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
db = FAISS.from_documents(chunks, embedding=embeddings)
return db
retriever = embed_uploaded_files(uploaded_files) if uploaded_files else None
# --- Streaming Response ---
def generate_response(prompt_text):
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
thread = Thread(target=model.generate, kwargs={
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"max_new_tokens": 1024,
"temperature": 0.7,
"top_p": 0.9,
"repetition_penalty": 1.1,
"do_sample": True,
"streamer": streamer
})
thread.start()
return streamer
# --- Avatars & Messages ---
USER_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/9904d9a0d445ab0488cf7395cb863cce7621d897/USER_AVATAR.png"
BOT_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/991f4c6e4e1dc7a8e24876ca5aae5228bcdb4dba/Ataliba_Avatar.jpg"
if "messages" not in st.session_state:
st.session_state.messages = []
for msg in st.session_state.messages:
avatar = USER_AVATAR if msg["role"] == "user" else BOT_AVATAR
with st.chat_message(msg["role"], avatar=avatar):
st.markdown(msg["content"])
# --- Chat UI ---
if prompt := st.chat_input("Ask something based on uploaded documents..."):
st.chat_message("user", avatar=USER_AVATAR).markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
context = ""
if retriever:
docs = retriever.similarity_search(prompt, k=3)
context = "\n\n".join([d.page_content for d in docs])
full_prompt = build_prompt(st.session_state.messages, context=context)
with st.chat_message("assistant", avatar=BOT_AVATAR):
start_time = time.time()
streamer = generate_response(full_prompt)
container = st.empty()
answer = ""
for chunk in streamer:
answer += chunk
container.markdown(answer + "▌", unsafe_allow_html=True)
container.markdown(answer)
st.session_state.messages.append({"role": "assistant", "content": answer}) |