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import gradio as gr
# Placeholder for model loading (adjust as needed for your specific models)
# In a real application, these would load actual models
def load_model(model_name):
print(f"Loading {model_name}...") # Indicate model loading
# Simulate different model behaviors (replace with actual model logic)
if model_name == "DeepSeek-R1-Distill-Qwen-32B":
return lambda input_text, history: f"Distilled Model Response to: {input_text}"
elif model_name == "DeepSeek-R1":
return lambda input_text, history: f"Base Model Response to: {input_text}"
elif model_name == "DeepSeek-R1-Zero":
return lambda input_text, history: f"Zero Model Response to: {input_text}"
else:
return lambda input_text, history: f"Default Response to: {input_text}"
# Load the models (placeholder functions here)
deepseek_r1_distill = load_model("DeepSeek-R1-Distill-Qwen-32B")
deepseek_r1 = load_model("DeepSeek-R1")
deepseek_r1_zero = load_model("DeepSeek-R1-Zero")
# --- Chatbot function ---
def chatbot(input_text, history, model_choice, system_message, max_new_tokens, temperature, top_p):
history = history or []
print(f"Input: {input_text}, History: {history}, Model: {model_choice}")
# Choose the model based on user selection
if model_choice == "DeepSeek-R1-Distill-Qwen-32B":
model_function = deepseek_r1_distill
elif model_choice == "DeepSeek-R1":
model_function = deepseek_r1
elif model_choice == "DeepSeek-R1-Zero":
model_function = deepseek_r1_zero
else:
model_function = lambda x, h: "Please select a model."
# Simulate model response with parameters (replace with actual model inference)
# In a real application, you would pass these parameters to your model
response = model_function(input_text, history)
response = f"**System Message:** {system_message}\n\n**Model Response:** {response}\n\n" \
f"**Parameters Used:**\n- Max New Tokens: {max_new_tokens}\n- Temperature: {temperature}\n- Top-p: {top_p}"
history.append((input_text, response))
return history, history, "" # Update both chatbot output and state
# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo: # Apply a theme
gr.Markdown(
"""
# DeepSeek Chatbot
Created by [ruslanmv.com](https://ruslanmv.com/)
This is a demo of different DeepSeek models. Select a model, type your message, and click "Submit".
You can also adjust optional parameters like system message, max new tokens, temperature, and top-p.
"""
)
with gr.Row():
with gr.Column(scale=4): # Make chatbot take more space
chatbot_output = gr.Chatbot(label="DeepSeek Chatbot", height=500)
msg = gr.Textbox(label="Your Message", placeholder="Type your message here...")
with gr.Row():
submit_btn = gr.Button("Submit", variant="primary")
clear_btn = gr.ClearButton([msg, chatbot_output])
with gr.Column(scale=1):
model_choice = gr.Radio(
choices=["DeepSeek-R1-Distill-Qwen-32B", "DeepSeek-R1", "DeepSeek-R1-Zero"],
label="Choose a Model",
value="DeepSeek-R1" # Default model
)
with gr.Accordion("Optional Parameters", open=False):
system_message = gr.Textbox(
label="System Message",
value="You are a friendly Chatbot created by ruslanmv.com",
lines=2,
)
max_new_tokens = gr.Slider(
minimum=1, maximum=4000, value=200, label="Max New Tokens"
)
temperature = gr.Slider(
minimum=0.10, maximum=4.00, value=0.70, label="Temperature"
)
top_p = gr.Slider(
minimum=0.10, maximum=1.00, value=0.90, label="Top-p (nucleus sampling)"
)
# Maintain chat history
chat_history = gr.State([])
# Event handling
submit_btn.click(
chatbot,
[msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p],
[chatbot_output, chat_history, msg],
)
msg.submit(
chatbot,
[msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p],
[chatbot_output, chat_history, msg],
)
# Launch the demo
if __name__ == "__main__":
demo.launch() |