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import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset # Import datasets library
# Load the PleIAs/common_corpus dataset
common_corpus = load_dataset("PleIAs/common_corpus")
# Function to retrieve an example from the dataset
def get_example_from_corpus(dataset, index):
if "train" in dataset:
example = dataset["train"][index]
return example
else:
raise ValueError("Dataset does not have a 'train' split.")
# Initialize inference client
client = InferenceClient("unsloth/Llama-3.2-1B-Instruct")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
# Add historical interactions
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 user's message
messages.append({"role": "user", "content": message})
# Get response from model
response = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
).choices[0].message.content
return response
# Example usage of the dataset
example_data = get_example_from_corpus(common_corpus, index=0)
print("Example from PleIAs/common_corpus:", example_data)
# Gradio ChatInterface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot. Your name is Juninho.", label="System message"),
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)",
),
],
)
if __name__ == "__main__":
demo.launch() |