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import os |
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import streamlit as st |
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from transformers import AutoModel, AutoTokenizer |
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st.title("HuggingFace Model Loader & Saver") |
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st.write("Load a model from HuggingFace and save it locally. Edit parameters below:") |
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model_name = st.text_input("Model Name", value="openai-gpt", help="Enter the HuggingFace model name (e.g., openai-gpt)") |
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save_dir = st.text_input("Save Directory", value="./hugging", help="Local directory to save the model") |
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additional_models = st.multiselect( |
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"Additional Models", |
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options=["bert-base-uncased", "gpt2", "roberta-base"], |
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help="Select additional models to load and save" |
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) |
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if st.button("Load and Save Model"): |
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st.write("### Processing Primary Model") |
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try: |
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st.write(f"Loading **{model_name}** ...") |
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model = AutoModel.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model_save_path = os.path.join(save_dir, model_name.replace("/", "_")) |
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os.makedirs(model_save_path, exist_ok=True) |
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model.save_pretrained(model_save_path) |
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st.success(f"Model **{model_name}** saved to `{model_save_path}`") |
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except Exception as e: |
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st.error(f"Error loading/saving model **{model_name}**: {e}") |
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if additional_models: |
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st.write("### Processing Additional Models") |
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for m in additional_models: |
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try: |
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st.write(f"Loading **{m}** ...") |
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model = AutoModel.from_pretrained(m) |
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tokenizer = AutoTokenizer.from_pretrained(m) |
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model_save_path = os.path.join(save_dir, m.replace("/", "_")) |
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os.makedirs(model_save_path, exist_ok=True) |
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model.save_pretrained(model_save_path) |
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st.success(f"Model **{m}** saved to `{model_save_path}`") |
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except Exception as e: |
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st.error(f"Error loading/saving model **{m}**: {e}") |
