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# app.py
import streamlit as st
from models import demo_qwen, demo_r1, demo_zero
st.set_page_config(page_title="DeepSeek Chatbot", layout="centered")
# A helper function to pick the correct Gradio interface
def select_demo(model_name):
if model_name == "DeepSeek-R1-Distill-Qwen-32B":
return demo_qwen
elif model_name == "DeepSeek-R1":
return demo_r1
elif model_name == "DeepSeek-R1-Zero":
return demo_zero
else:
return demo_qwen # default fallback
# Title of the Streamlit app
st.title("DeepSeek Chatbot")
# Sidebar or main area for parameter selection
st.subheader("Model and Parameters")
model_name = st.selectbox(
"Select Model",
["DeepSeek-R1-Distill-Qwen-32B", "DeepSeek-R1", "DeepSeek-R1-Zero"]
)
# Optional parameter: System message
system_message = st.text_area(
"System Message",
value="You are a friendly Chatbot created by ruslanmv.com",
height=80
)
# Optional parameter: max new tokens
max_new_tokens = st.slider(
"Max new tokens",
min_value=1,
max_value=4000,
value=512,
step=1
)
# Optional parameter: temperature
temperature = st.slider(
"Temperature",
min_value=0.10,
max_value=4.00,
value=0.80,
step=0.05
)
# Optional parameter: top-p
top_p = st.slider(
"Top-p (nucleus sampling)",
min_value=0.10,
max_value=1.00,
value=0.90,
step=0.05
)
# A text area for user input
st.subheader("Chat")
user_prompt = st.text_area("Your message:", value="", height=100)
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
# Button to send user prompt
if st.button("Send"):
if user_prompt.strip():
# Retrieve the correct Gradio demo
demo = select_demo(model_name)
# Here we assume the Gradio interface has a function signature for `.predict()`
# that accepts text plus generation parameters in some order.
# Many huggingface-style Gradio demos simply take a single text prompt,
# but it depends entirely on how `demo` is defined in your Gradio code.
#
# If the interface has multiple inputs in a specific order, you might do something like:
# response = demo.predict(system_message, user_prompt, max_new_tokens, temperature, top_p)
#
# Or if it only expects a single string, you might combine them:
# combined_prompt = f"System: {system_message}\nUser: {user_prompt}"
# response = demo.predict(combined_prompt, max_new_tokens, temperature, top_p)
#
# The exact call depends on your Gradio block's input signature.
# For illustrative purposes, let's assume a simple signature:
# demo.predict(prompt: str, max_new_tokens: int, temperature: float, top_p: float)
# and we inject the system message on top:
combined_prompt = f"{system_message}\n\nUser: {user_prompt}"
try:
response = demo.predict(
combined_prompt,
max_new_tokens,
temperature,
top_p
)
except Exception as e:
response = f"Error: {e}"
st.session_state["chat_history"].append(("User", user_prompt))
st.session_state["chat_history"].append(("Assistant", response))
st.experimental_rerun()
# Display conversation
if st.session_state["chat_history"]:
for role, text in st.session_state["chat_history"]:
if role == "User":
st.markdown(f"**{role}:** {text}")
else:
st.markdown(f"**{role}:** {text}")
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