HG_model+dataset+UI_pipeline_Template.txt

from transformers import pipeline
from datasets import load_dataset
import gradio as gr

# --- Hugging Face Pipeline Setup ---
# Replace 'model_id' with the identifier of the model you wish to use
model_id = "declare-lab/flan-alpaca-gpt4-xl"
text_generator = pipeline(model=model_id)

# --- Dataset Loading (Optional) ---
# Replace with the dataset of your choice
dataset_name = "luisotorres/wikipedia-crypto-articles"  # Example dataset
dataset_split = 'train'  # Choose from 'train', 'test', 'validation', etc.
dataset = load_dataset(dataset_name, split=dataset_split)

# --- Text Generation Function ---
def generate_text(prompt, max_tokens=128, system_message=""):
    # Generating text using the pipeline
    generated_text = text_generator(prompt, max_length=max_tokens, do_sample=True)[0]["generated_text"]

    # Incorporating the system message
    full_message = system_message + "\n\nGenerated Text:\n" + generated_text
    return full_message

# --- Gradio Interface Setup ---
iface = gr.Interface(
    fn=generate_text, 
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Enter Prompt Here..."), 
        gr.inputs.Slider(minimum=1, maximum=256, default=128, label="Max Tokens"),
        gr.inputs.Textbox(label="System Message", placeholder="Enter a system message here...")
    ], 
    outputs="text"
)

# --- Launch the Interface ---
iface.launch()