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import streamlit as st
# Your existing demos
from assist.chat import chat as embed_chat
from assist.bayes_chat import bayes_chat
from assist.transformer_demo import transformer_next
# DeepSeek imports
from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
# Retrieval imports
from sentence_transformers import SentenceTransformer
import torch
st.set_page_config(page_title="RepoSage All-in-One Demo", layout="wide")
st.title("🤖 RepoSage Unified Demo")
# Cache and load DeepSeek-R1
@st.cache_resource
def load_deepseek():
model_name = "deepseek-ai/DeepSeek-Coder-1.3B-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return TextGenerationPipeline(model=model, tokenizer=tokenizer)
deepseek_gen = load_deepseek()
# Cache and load training corpus passages
@st.cache_data
def load_passages(path="RepoSage Training.txt"):
text = open(path, encoding="utf8").read()
paras = [p.strip() for p in text.split("\n\n") if p.strip()]
return paras
# Cache and embed passages
@st.cache_resource
def embed_passages(passages):
encoder = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = encoder.encode(passages, convert_to_tensor=True)
return encoder, passages, embeddings
# Prepare RAG resources
_passages = load_passages()
_encoder, passages, passage_embs = embed_passages(_passages)
# User input
title = st.text_input("Enter your question or prompt below:")
# Define columns for five demos
col1, col2, col3, col4, col5 = st.columns(5)
# Math demo in col1
with col1:
if st.button("DeepSeek-R1 Math Demo"):
if not title.strip():
st.warning("Please enter a prompt first.")
else:
prompt = f"You are an expert math tutor. Compute the derivative of f(x) = {title} step by step using the product rule. Solution:\n"
with st.spinner("Working it out…"):
out = deepseek_gen(prompt, max_new_tokens=80, do_sample=False, temperature=0.0)
st.code(out[0]["generated_text"], language="text")
# RAG-augmented demo in col2
with col2:
if st.button("DeepSeek-R1 RAG Demo"):
if not title.strip():
st.warning("Please enter a question first.")
else:
q_emb = _encoder.encode(title, convert_to_tensor=True)
sims = torch.nn.functional.cosine_similarity(q_emb.unsqueeze(0), passage_embs)
topk = torch.topk(sims, k=min(3, len(passages))).indices.tolist()
context = "\n\n".join(passages[i] for i in topk)
prompt = f"Use these notes to answer the question:\n\n{context}\n\nQ: {title}\nA:"
with st.spinner("Retrieving & generating…"):
out = deepseek_gen(prompt, max_new_tokens=100, do_sample=False)
st.write(out[0]["generated_text"])
# Embedding Q&A in col3
with col3:
if st.button("Embedding Q&A"):
st.write(embed_chat(title))
# Bayesian Q&A in col4
with col4:
if st.button("Bayesian Q&A"):
st.write(bayes_chat(title))
# Transformer Demo in col5
with col5:
if st.button("Transformer Demo"):
st.write(transformer_next(title))
st.markdown("---")
st.caption("DeepSeek-R1 Math, RAG, Embedding, Bayesian & Transformer demos all in one place ✅")
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