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import gradio as gr
from tokenizer import StockBPE
import json
# Initialize and load the tokenizer
tokenizer = StockBPE()
try:
tokenizer.load("stock_bpe")
print("Tokenizer loaded successfully!")
except Exception as e:
print(f"Error loading tokenizer: {e}")
# Fallback for initial build if files aren't there yet
pass
def analyze_text(text):
if not text:
return "Please enter text", "0", "0.00x"
# Encode
tokens = tokenizer.encode(text)
# Decode
decoded = tokenizer.decode(tokens)
# Stats
original_len = len(text.encode('utf-8'))
token_len = len(tokens)
ratio = original_len / token_len if token_len > 0 else 0
# Format output
token_str = str(tokens)
if len(token_str) > 1000:
token_str = token_str[:1000] + "... (truncated)"
return token_str, decoded, f"{ratio:.2f}x"
# Example data
examples = [
["TECH|AAPL|2020-11|MON|UNDER200|OPEN:113.9|HIGH:117.8|LOW:113.7|CLOSE:115.9|VOL:HIGH"],
["FIN|JPM|2023-05|FRI|UNDER150|OPEN:135.2|HIGH:136.5|LOW:134.8|CLOSE:135.9|VOL:MED"],
["TECH|MSFT|2024-01|WED|OVER300|OPEN:380.5|HIGH:385.2|LOW:379.0|CLOSE:384.8|VOL:HIGH"]
]
# Create Interface
iface = gr.Interface(
fn=analyze_text,
inputs=gr.Textbox(lines=3, placeholder="Enter stock data here...", label="Input Text"),
outputs=[
gr.Textbox(label="Tokens IDs"),
gr.Textbox(label="Decoded Back (Verification)"),
gr.Label(label="Compression Ratio")
],
title="📈 Stock Market BPE Tokenizer",
description="A custom BPE tokenizer trained on financial time-series data. Enter stock data to see how it gets compressed!",
examples=examples,
theme="huggingface"
)
if __name__ == "__main__":
iface.launch()
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