app.py

import gradio as gr
from huggingface_hub import InferenceClient
import streamlit as st
from transformers import pipeline
from datasets import load_dataset

force_download=True
# Initialize the Hugging Face InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Initialize text-generation pipeline with the model for Streamlit
model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
pipe = pipeline("text-generation", model=model_name)

# Load the dataset
ds = load_dataset("refugee-law-lab/canadian-legal-data", "default", split="train")


# Gradio Function
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    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]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


# Gradio interface setup
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", 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)",
        ),
    ],
)


# Streamlit interface setup
def streamlit_interface():
    st.title("Canadian Legal Text Generator")
    st.write("Enter a prompt related to Canadian legal data and generate text using Llama-3.1.")

    # Show dataset sample
    st.subheader("Sample Data from Canadian Legal Dataset:")
    st.write(ds[:5])  # Displaying the first 5 rows of the dataset

    # Prompt input
    prompt = st.text_area("Enter your prompt:", placeholder="Type something...")

    if st.button("Generate Response"):
        if prompt:
            # Generate text based on the prompt
            with st.spinner("Generating response..."):
                generated_text = pipe(prompt, max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
                st.write("**Generated Text:**")
                st.write(generated_text)
        else:
            st.write("Please enter a prompt to generate a response.")


# Running Gradio and Streamlit interfaces
if __name__ == "__main__":
    st.sidebar.title("Choose an Interface")
    interface = st.sidebar.radio("Select", ("Streamlit", "Gradio"))

    if interface == "Streamlit":
        streamlit_interface()
    else:
        demo.launch()