import gradio as gr
import pandas as pd
from sentence_transformers import SentenceTransformer

# 1. Load a pretrained SentenceTransformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

def clean_and_embed(text: str):
    # 2. Clean: remove non-ASCII & lowercase
    clean = text.encode('ascii', 'ignore').decode().lower()
    
    # 3. Tokenize via the model’s tokenizer
    tokens = model.tokenizer.tokenize(clean)
    
    # 4. Get the sentence embedding
    emb = model.encode(clean, convert_to_numpy=True)
    
    # 5. Build a DataFrame: one row (the sentence) × embedding dims
    df = pd.DataFrame(
        [emb],
        index=["sentence_embedding"],
        columns=[f"dim_{i}" for i in range(emb.shape[0])]
    )
    
    return " ".join(tokens), df

# 6. Gradio interface
iface = gr.Interface(
    fn=clean_and_embed,
    inputs=gr.Textbox(lines=2, placeholder="Type your text here…"),
    outputs=[
        gr.Textbox(label="Tokens"),
        gr.Dataframe(label="Sentence Embedding Vector")
    ],
    title="ASCII‑Clean + SentenceTransformer",
    description="Cleans input, tokenizes with a SentenceTransformer tokenizer, and shows the  sentence embedding."
)

if __name__ == "__main__":
    iface.launch()