Create app.py

app.py

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