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| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| # import os | |
| # import torch | |
| # | |
| # # Assuming models are stored in a 'models' directory | |
| # MODELS_DIR = "models" | |
| # | |
| # | |
| # def get_local_models(): | |
| # if not os.path.exists(MODELS_DIR): | |
| # os.makedirs(MODELS_DIR) | |
| # return [d for d in os.listdir(MODELS_DIR) if os.path.isdir(os.path.join(MODELS_DIR, d))] | |
| # | |
| # | |
| # def download_model(model_name): | |
| # try: | |
| # tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # | |
| # # Save the model and tokenizer | |
| # save_path = os.path.join(MODELS_DIR, model_name.split('/')[-1]) | |
| # tokenizer.save_pretrained(save_path) | |
| # model.save_pretrained(save_path) | |
| # | |
| # return f"Successfully downloaded model: {model_name}" | |
| # except Exception as e: | |
| # return f"Failed to download model: {str(e)}" | |
| # | |
| # | |
| # def run_inference(model_name, prompt): | |
| # try: | |
| # model_path = os.path.join(MODELS_DIR, model_name) | |
| # tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| # model = AutoModelForCausalLM.from_pretrained(model_path) | |
| # | |
| # # Use GPU if available | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model.to(device) | |
| # | |
| # # Create a text-generation pipeline | |
| # text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) | |
| # | |
| # # Generate text | |
| # result = text_generator(prompt, max_length=100, num_return_sequences=1) | |
| # | |
| # return result[0]['generated_text'] | |
| # except Exception as e: | |
| # return f"Error running inference: {str(e)}" | |
| # | |
| # | |
| # def create_huggingface_tab(): | |
| # with gr.Tab("Hugging Face Transformers"): | |
| # gr.Markdown("# Hugging Face Transformers Model Management") | |
| # | |
| # with gr.Row(): | |
| # model_list = gr.Dropdown(label="Available Models", choices=get_local_models()) | |
| # refresh_button = gr.Button("Refresh Model List") | |
| # | |
| # with gr.Row(): | |
| # new_model_name = gr.Textbox(label="Model to Download (e.g., 'gpt2' or 'EleutherAI/gpt-neo-1.3B')") | |
| # download_button = gr.Button("Download Model") | |
| # | |
| # download_output = gr.Textbox(label="Download Status") | |
| # | |
| # with gr.Row(): | |
| # run_model = gr.Dropdown(label="Model to Run", choices=get_local_models()) | |
| # prompt = gr.Textbox(label="Prompt") | |
| # run_button = gr.Button("Run Inference") | |
| # | |
| # run_output = gr.Textbox(label="Model Output") | |
| # | |
| # def update_model_lists(): | |
| # models = get_local_models() | |
| # return gr.update(choices=models), gr.update(choices=models) | |
| # | |
| # refresh_button.click(update_model_lists, outputs=[model_list, run_model]) | |
| # download_button.click(download_model, inputs=[new_model_name], outputs=[download_output]) | |
| # run_button.click(run_inference, inputs=[run_model, prompt], outputs=[run_output]) |