Upload digitwin_rag_qwen_app.py

digitwin_rag_qwen_app.py

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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
+Context:
+{context}<|im_end|>
+"
+    for msg in messages:
+        role = msg["role"]
+        prompt += f"<|im_start|>{role}\n{msg['content']}<|im_end|>
+"
+    prompt += "<|im_start|>assistant
+"
+    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})