Scaffold DeepSeek-R1 Streamlit demo

app.py

@@ -1,19 +1,52 @@
 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
 
-st.title("RepoSage Chatbot Demo")
+# DeepSeek imports
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
+
+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()
 
-question = st.text_input("Enter your question below:")
+# User input
+question = st.text_input("Enter your question or prompt below:")
+
+# Four buttons side by side, with DeepSeek first
+col1, col2, col3, col4 = st.columns(4)
 
-col1, col2, col3 = st.columns(3)
 with col1:
+    if st.button("DeepSeek-R1 Demo"):
+        if not question.strip():
+            st.warning("Please enter a prompt first.")
+        else:
+            with st.spinner("Generating with DeepSeek…"):
+                out = deepseek_gen(question, max_new_tokens=100, do_sample=True)
+            st.code(out[0]["generated_text"], language="text")
+
+with col2:
     if st.button("Embedding Q&A"):
         st.write(embed_chat(question))
-with col2:
+
+with col3:
     if st.button("Bayesian Q&A"):
         st.write(bayes_chat(question))
-with col3:
+
+with col4:
     if st.button("Transformer Demo"):
         st.write(transformer_next(question))
+
+st.markdown("---")
+st.caption("DeepSeek-R1, Embedding, Bayesian & Transformer demos all in one place ✅")

requirements.txt

@@ -5,3 +5,4 @@ streamlit==1.46.0
 typer==0.16.0
 rich==14.0.0
 torch==2.7.1
+transformers