import gradio as gr
import transformers
import torch
import re

# Initialize the model
model_id = "Detsutut/Igea-350M-v0.0.1"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16}
)

# Define the function to generate text
def generate_text(input_text, max_new_tokens, temperature, top_p, split_output):
    if split_output:
        max_new_tokens=30
        top_p=0.95
    output = pipeline(
        input_text,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        top_p=top_p,
    )
    generated_text = output[0]['generated_text']
    if split_output:
        sentences = re.split('(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s)', generated_text)
        if sentences:
            return sentences[0] + '.'
    return 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() as iface:
    gr.Markdown("# Igea 350M Text Generation Interface")
    gr.Markdown("⚠️ 🐢💬 This model runs on a hardware-limited, free-tier HuggingFace space, resulting in a low output token throughput (approx. 1 token/s)")
    input_text.render()
    with gr.Accordion("Advanced Options", open=False):
        max_new_tokens.render()
        temperature.render()
        top_p.render()
        split_output.render()
    output = gr.Textbox(label="Generated Text")

    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()