Create app.py

app.py

@@ -0,0 +1,40 @@
+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()