app.py

import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset

# Safely load the PleIAs/common_corpus dataset
def load_common_corpus():
    try:
        print("Loading dataset...")
        dataset = load_dataset("PleIAs/common_corpus")
        print("Dataset loaded successfully!")
        return dataset
    except Exception as e:
        print(f"Error loading dataset: {e}")
        return None

common_corpus = load_common_corpus()

# Retrieve an example safely
def get_example_from_corpus(dataset, index):
    if dataset and "train" in dataset:
        try:
            return dataset["train"][index]
        except IndexError:
            print("Index out of range for dataset.")
            return {"text": "No example available"}
    else:
        print("Dataset not loaded correctly.")
        return {"text": "Dataset not available."}

# Safely initialize the inference client
def initialize_client():
    try:
        print("Initializing inference client...")
        client = InferenceClient("unsloth/Llama-3.2-1B-Instruct")
        print("Inference client initialized successfully!")
        return client
    except Exception as e:
        print(f"Error initializing inference client: {e}")
        return None

client = initialize_client()

# Chatbot response logic
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    if not client:
        return "Error: Inference client not initialized."

    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 message
    messages.append({"role": "user", "content": message})

    try:
        print("Sending request to model...")
        response = client.chat_completion(
            messages,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
        ).choices[0].message.content
        print("Response received successfully!")
        return response
    except Exception as e:
        print(f"Error during inference: {e}")
        return "An error occurred while generating a response."

# Example: Retrieve an entry from the dataset
example_data = get_example_from_corpus(common_corpus, 0)
print("Example from dataset:", example_data)

# Gradio interface
def launch_demo():
    try:
        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)",
                ),
            ],
        )
        demo.launch()
    except Exception as e:
        print(f"Error launching Gradio app: {e}")

if __name__ == "__main__":
    launch_demo()