Update app.py

app.py

@@ -1,45 +1,63 @@
 import os
 import gradio as gr
 from transformers import AutoModel, AutoTokenizer
-from fastapi import FastAPI
 
 def process_models(model_name, save_dir, additional_models):
     log_lines = []
     
     # Process primary model
-    log_lines.append(f"🚀 Loading model: **{model_name}**")
+    log_lines.append(f"🚀 Loading model: {model_name}")
     try:
         model = AutoModel.from_pretrained(model_name)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         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)
-        log_lines.append(f"✅ Saved **{model_name}** to `{model_save_path}`")
+        log_lines.append(f"✅ Saved {model_name} to {model_save_path}")
     except Exception as e:
-        log_lines.append(f"❌ Error with **{model_name}**: {e}")
+        log_lines.append(f"❌ Error with {model_name}: {e}")
     
     # Process additional models if any
     if additional_models:
         for m in additional_models:
-            log_lines.append(f"🚀 Loading model: **{m}**")
+            log_lines.append(f"🚀 Loading model: {m}")
             try:
                 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)
-                log_lines.append(f"✅ Saved **{m}** to `{model_save_path}`")
+                log_lines.append(f"✅ Saved {m} to {model_save_path}")
             except Exception as e:
-                log_lines.append(f"❌ Error with **{m}**: {e}")
+                log_lines.append(f"❌ Error with {m}: {e}")
     
     return "\n".join(log_lines)
 
-# A Mermaid diagram embedded in Markdown as a mini-glossary.
-mermaid_glossary = """
-```mermaid
-graph LR
-A[🚀 Model Input] --> B[Load Model]
-B --> C[💾 Save Model]
-D[🧩 Additional Models] --> B
-```
-"""
+with gr.Blocks() as demo:
+    gr.Markdown("# HuggingFace Model Loader & Saver")
+    gr.Markdown("Load and save HuggingFace models locally using Transformers.")
+    
+    with gr.Row():
+        model_name_input = gr.Textbox(label="🚀 Model", value="openai-gpt", placeholder="Enter model name")
+        save_dir_input = gr.Textbox(label="💾 Save Dir", value="./hugging", placeholder="Enter save directory")
+    
+    additional_models_input = gr.Dropdown(
+        label="🧩 Additional Models", 
+        choices=["bert-base-uncased", "gpt2", "roberta-base"],
+        value=[], 
+        multiselect=True,
+        info="Select additional models"
+    )
+    
+    run_button = gr.Button("Load & Save Model")
+    output_log = gr.Textbox(label="Output Log", lines=10)
+    
+    run_button.click(
+        fn=process_models, 
+        inputs=[model_name_input, save_dir_input, additional_models_input], 
+        outputs=output_log
+    )
+
+if __name__ == "__main__":
+    # Launch the Gradio app. Hugging Face Spaces will execute this file with python.
+    demo.launch(server_name="0.0.0.0", server_port=7860)