app.py

from transformers import AutoTokenizer
import gradio as gr

def formatarr(input):
   return "["+",".join(str(x) for x in input)+"]"

def tokenize(input_text):
    llama_tokens = llama_tokenizer(input_text, add_special_tokens=True)["input_ids"]

    llama3_tokens = len(
        llama3_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    mistral_tokens = len(
        mistral_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    gpt2_tokens = len(
        gpt2_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    gpt_neox_tokens = len(
        gpt_neox_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    falcon_tokens = len(
        falcon_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    phi2_tokens = len(
        phi2_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    t5_tokens = len(
        t5_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    gemma_tokens = len(
        gemma_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    command_r_tokens = len(
        command_r_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    qwen_tokens = len(
        qwen_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )
    codeqwen_tokens = len(
        codeqwen_tokenizer(input_text, add_special_tokens=True)["input_ids"]
    )

    results = {
        "LLaMa-1/LLaMa-2": len(llama_tokens),
        "LLaMa-3": llama3_tokens,
        "Mistral": mistral_tokens,
        "GPT-2/GPT-J": gpt2_tokens,
        "GPT-NeoX": gpt_neox_tokens,
        "Falcon": falcon_tokens,
        "Phi-1/Phi-2": phi2_tokens,
        "T5": t5_tokens,
        "Gemma": gemma_tokens,
        "Command-R": command_r_tokens,
        "Qwen/Qwen1.5": qwen_tokens,
        "CodeQwen": codeqwen_tokens,
    }

    results2 = {
        "LLaMa-1/LLaMa-2": formatarr(llama_tokens),
        "LLaMa-3": llama3_tokens,
        "Mistral": mistral_tokens,
        "GPT-2/GPT-J": gpt2_tokens,
        "GPT-NeoX": gpt_neox_tokens,
        "Falcon": falcon_tokens,
        "Phi-1/Phi-2": phi2_tokens,
        "T5": t5_tokens,
        "Gemma": gemma_tokens,
        "Command-R": command_r_tokens,
        "Qwen/Qwen1.5": qwen_tokens,
        "CodeQwen": codeqwen_tokens,
    }

    # Sort the results in descending order based on token length
    sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)

    lens = "\n".join([f"{model}: {tokens}" for model, tokens in sorted_results])
    toks = "\n".join([f"{model}: {tokens}" for model, tokens in results2])    
    return lens + "\n" + toks


if __name__ == "__main__":
    llama_tokenizer = AutoTokenizer.from_pretrained(
        "TheBloke/Llama-2-7B-fp16"
    )
    llama3_tokenizer = AutoTokenizer.from_pretrained(
        "unsloth/llama-3-8b"
    )
    mistral_tokenizer = AutoTokenizer.from_pretrained(
        "mistral-community/Mistral-7B-v0.2"
    )
    gpt2_tokenizer = AutoTokenizer.from_pretrained(
        "gpt2"
    )
    gpt_neox_tokenizer = AutoTokenizer.from_pretrained(
        "EleutherAI/gpt-neox-20b"
    )
    falcon_tokenizer = AutoTokenizer.from_pretrained(
        "tiiuae/falcon-7b"
    )
    phi2_tokenizer = AutoTokenizer.from_pretrained(
        "microsoft/phi-2"
    )
    t5_tokenizer = AutoTokenizer.from_pretrained(
        "google/flan-t5-xxl"
    )
    gemma_tokenizer = AutoTokenizer.from_pretrained(
        "alpindale/gemma-2b"
    )
    command_r_tokenizer = AutoTokenizer.from_pretrained(
        "CohereForAI/c4ai-command-r-plus"
    )
    qwen_tokenizer = AutoTokenizer.from_pretrained(
        "Qwen/Qwen1.5-7B"
    )
    codeqwen_tokenizer = AutoTokenizer.from_pretrained(
        "Qwen/CodeQwen1.5-7B"
    )

    iface = gr.Interface(
        fn=tokenize, inputs=gr.Textbox(label="Input Text", lines=12), outputs="text"
    )
    iface.launch()