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import gradio as gr
from huggingface_hub import InferenceClient
# Function to create InferenceClient dynamically based on model selection
def get_client(model_name):
return InferenceClient(model_name)
def respond(
message,
history: list[tuple[str, str]],
max_tokens,
temperature,
top_p,
model_name, # Added model_name to the function arguments
):
# Statically defined system message
system_message = "You are a friendly Chatbot."
# Create client for the selected model
client = get_client(model_name)
# Check if the model is one of the problematic models
if model_name in ["indonlp/cendol-mt5-small-inst", "indonlp/cendol-mt5-small-chat"]:
# For these models, we simply concatenate the conversation into a single string
history_str = ""
for user_msg, assistant_msg in history:
if user_msg:
history_str += f"{user_msg}\n"
if assistant_msg:
history_str += f"{assistant_msg}\n"
# Add the latest user message
history_str += f"{message}\n"
# Pass the entire conversation history as a plain text prompt
response = client.text_generation(
history_str, # Single string as input
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
# Since response is a string, return it directly
full_response = response
else:
# For other models, we use a structured format with roles
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Add the latest user message
messages.append({"role": "user", "content": message})
# Make the request
response = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=False
)
# Extract the full response for chat models
full_response = response.choices[0].message["content"]
return full_response
# Gradio ChatInterface setup with static system message and no Textbox for system message
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95, step=0.05, label="Top-p (nucleus sampling)"
),
# Dropdown to select model
gr.Dropdown(
choices=[
"meta-llama/Meta-Llama-3-8B-Instruct",
"mistralai/Mistral-7B-Instruct-v0.3",
"HuggingFaceH4/zephyr-7b-beta"
],
value="meta-llama/Meta-Llama-3-8B-Instruct",
label="Choose Model"
),
],
)
if __name__ == "__main__":
demo.launch() |