Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import torch
 import os
-import tempfile
 import time
 from threading import Thread
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
@@ -9,13 +8,7 @@ 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.retrievers import BM25Retriever, EnsembleRetriever
 from langchain.schema import Document
-from langchain.docstore.document import Document as LangchainDocument
-
-# --- Avatars ---
-USER_AVATAR = "👤"
-BOT_AVATAR = "🤖"
 
 # --- HF Token ---
 HF_TOKEN = st.secrets["HF_TOKEN"]
@@ -24,86 +17,94 @@ HF_TOKEN = st.secrets["HF_TOKEN"]
 st.set_page_config(page_title="DigiTwin RAG", page_icon="📂", layout="centered")
 st.title("📂 DigiTs the Twin")
 
-# --- Sidebar ---
+# --- 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"])
-    hybrid_toggle = st.checkbox("🔀 Enable Hybrid Search", value=True)
-    clear_chat = st.button("🧹 Clear Chat History")
-
-# --- Session State ---
-if "messages" not in st.session_state or clear_chat:
-    st.session_state.messages = []
+    if uploaded_files:
+        st.success(f"{len(uploaded_files)} file(s) uploaded")
 
-# --- Load Model ---
+# --- Model Loading ---
 @st.cache_resource
 def load_model():
-    model_id = "tiiuae/falcon-7b-instruct"
-    tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
-    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-# --- Load & Chunk Documents ---
-def process_documents(files):
-    documents = []
-    for file in files:
-        suffix = ".pdf" if file.name.endswith(".pdf") else ".txt"
-        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_file:
-            tmp_file.write(file.read())
-            tmp_file_path = tmp_file.name
-        loader = PyPDFLoader(tmp_file_path) if suffix == ".pdf" else TextLoader(tmp_file_path)
-        documents.extend(loader.load())
-    return documents
-
-def chunk_documents(documents):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    return splitter.split_documents(documents)
-
-def build_retrievers(chunks):
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    faiss_vectorstore = FAISS.from_documents(chunks, embeddings)
-    faiss_retriever = faiss_vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 5})
-    bm25_retriever = BM25Retriever.from_documents([LangchainDocument(page_content=d.page_content) for d in chunks])
-    bm25_retriever.k = 5
-    return faiss_retriever, EnsembleRetriever(retrievers=[faiss_retriever, bm25_retriever], weights=[0.5, 0.5])
-
-# --- Prompt Builder ---
-def build_prompt(history, context=""):
-    conversation = ""
-    for turn in history:
-        role = "User" if turn["role"] == "user" else "Assistant"
-        conversation += f"{role}: {turn['content']}\n"
-    return (
-        "You are DigiTwin, an expert advisor in asset integrity, reliability, inspection, and maintenance "
-        "of topside piping, structural, mechanical systems, floating units, pressure vessels (VII), and pressure safety devices (PSD's).\n\n"
-        f"Context:\n{context}\n\n"
-        f"{conversation}Assistant:"
+    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, a digital expert and senior topside engineer specializing in inspection and maintenance "
+    "of offshore piping systems, structural elements, mechanical equipment, floating production units, pressure vessels "
+    "(with emphasis on Visual Internal Inspection - VII), and pressure safety devices (PSDs). Rely on uploaded documents "
+    "and context to provide practical, standards-driven, and technically accurate responses. Your guidance reflects deep "
+    "field experience, industry regulations, and proven methodologies in asset integrity and reliability engineering."
+)
+
+
+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
 
-# --- Generator ---
-def generate_response(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, return_tensors="pt").to(model.device)
-    generation_kwargs = dict(**inputs, streamer=streamer, max_new_tokens=300)
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    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()
-    for token in streamer:
-        yield token
-
-# --- Main App ---
-retriever = None
-if uploaded_files:
-    with st.spinner("Processing documents..."):
-        docs = process_documents(uploaded_files)
-        chunks = chunk_documents(docs)
-        faiss, hybrid = build_retrievers(chunks)
-        retriever = hybrid if hybrid_toggle else faiss
-        st.success("Documents processed. Ask away!")
+    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:
-    with st.chat_message(msg["role"], avatar=USER_AVATAR if msg["role"] == "user" else BOT_AVATAR):
+    avatar = USER_AVATAR if msg["role"] == "user" else BOT_AVATAR
+    with st.chat_message(msg["role"], avatar=avatar):
         st.markdown(msg["content"])
 
 # --- Chat UI ---
@@ -113,12 +114,13 @@ if prompt := st.chat_input("Ask something based on uploaded documents..."):
 
     context = ""
     if retriever:
-        docs = retriever.get_relevant_documents(prompt)
+        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 = ""
@@ -126,4 +128,4 @@ if prompt := st.chat_input("Ask something based on uploaded documents..."):
             answer += chunk
             container.markdown(answer + "▌", unsafe_allow_html=True)
         container.markdown(answer)
-        st.session_state.messages.append({"role": "assistant", "content": answer})
+        st.session_state.messages.append({"role": "assistant", "content": answer})