import gradio as gr from ctransformers import AutoModelForCausalLM from transformers import AutoTokenizer, pipeline import torch import re # Initialize the model model = AutoModelForCausalLM.from_pretrained("Detsutut/Igea-1B-v0.0.1-Q4_K_M-GGUF", model_file="igea-1b-v0.0.1-q4_k_m.gguf", model_type="mistral", hf=True) tokenizer = AutoTokenizer.from_pretrained( "Detsutut/Igea-1B-v0.0.1") gen_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer ) # Define the function to generate text def generate_text(input_text, max_new_tokens=30, temperature=1, top_p=0.95, split_output=False): if split_output: max_new_tokens=30 top_p=0.95 output = gen_pipeline( input_text, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, return_full_text = False ) generated_text = output[0]['generated_text'] if split_output: sentences = re.split('(?{input_text}{generated_text}" # Create the Gradio interface input_text = gr.Textbox(lines=2, placeholder="Enter your text here...", label="Input Text") max_new_tokens = gr.Slider(minimum=1, maximum=200, value=30, step=1, label="Max New Tokens") temperature = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature") top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label="Top-p") split_output = gr.Checkbox(label="Quick single-sentence output", value=True) with gr.Blocks(css="#outbox { border-radius: 8px !important; border: 1px solid #e5e7eb !important; padding: 8px !important; text-align:center !important;}") as iface: gr.Markdown("# Igea Text Generation Interface ⚕️🩺") gr.Markdown("🐢💬 To guarantee a reasonable througput (<1 min to answer with default settings), this space employs a **GGUF quantized version of [Igea 1B](https://huggingface.co/bmi-labmedinfo/Igea-1B-v0.0.1)**, optimized for **hardware-limited, CPU-only machines** like the free-tier HuggingFace space.") gr.Markdown("⚠️ Read the **[bias, risks and limitations](https://huggingface.co/bmi-labmedinfo/Igea-1B-v0.0.1#%F0%9F%9A%A8%E2%9A%A0%EF%B8%8F%F0%9F%9A%A8-bias-risks-and-limitations-%F0%9F%9A%A8%E2%9A%A0%EF%B8%8F%F0%9F%9A%A8)** of Igea before use!") input_text.render() with gr.Accordion("Advanced Options", open=False): max_new_tokens.render() temperature.render() top_p.render() split_output.render() output = gr.HTML(label="Generated Text",elem_id="outbox") btn = gr.Button("Generate") btn.click(generate_text, [input_text, max_new_tokens, temperature, top_p, split_output], output) # Launch the interface if __name__ == "__main__": iface.launch(inline=True)