Create app py

app py

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+import gradio as gr
+import torch
+from safetensors.torch import save_file as torch_save_file
+import tensorflow as tf
+from safetensors.keras import save_model as keras_save_model
+import os
+import tempfile
+
+def convert_to_safetensors(framework, model_file):
+    """
+    Convert uploaded model files to SafeTensors format
+    """
+    if not model_file:
+        return gr.Error("Please upload a model file.")
+    
+    # Create a temporary output file
+    output_filename = "model.safetensors"
+    
+    try:
+        if framework == "PyTorch":
+            # Load PyTorch model weights safely
+            state_dict = torch.load(
+                model_file, 
+                map_location='cpu', 
+                weights_only=True
+            )
+            
+            # Handle case where full model is loaded instead of just state_dict
+            if hasattr(state_dict, 'state_dict'):
+                state_dict = state_dict.state_dict()
+            elif isinstance(state_dict, torch.nn.Module):
+                state_dict = state_dict.state_dict()
+            
+            # Save to SafeTensors format
+            torch_save_file(state_dict, output_filename)
+            
+            return output_filename
+            
+        elif framework == "TensorFlow":
+            # Load TensorFlow/Keras model
+            model = tf.keras.models.load_model(model_file)
+            
+            # Save to SafeTensors format
+            keras_save_model(model, output_filename)
+            
+            return output_filename
+            
+        else:
+            return gr.Error("Please select a valid framework (PyTorch or TensorFlow).")
+            
+    except Exception as e:
+        error_msg = f"{framework} Conversion Error: {str(e)}"
+        
+        if framework == "PyTorch":
+            error_msg += "\n\nTips:\n• Ensure the file is a valid PyTorch model (.pt, .pth)\n• Model should contain state_dict or be loadable with torch.load()"
+        elif framework == "TensorFlow":
+            error_msg += "\n\nTips:\n• Ensure the file is a valid TensorFlow model (.h5, SavedModel)\n• For SavedModel format, upload as a zip file containing the model directory"
+        
+        return gr.Error(error_msg)
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="SafeTensors Model Converter",
+    theme=gr.themes.Soft()
+) as iface:
+    
+    gr.Markdown("""
+    # 🔒 No-Code SafeTensors Model Creator
+    
+    Convert your machine learning models to the secure **SafeTensors** format with zero coding required!
+    
+    ## Why SafeTensors?
+    - **Security**: Prevents arbitrary code execution during model loading
+    - **Speed**: Faster loading times compared to pickle-based formats
+    - **Memory Efficiency**: Zero-copy deserialization
+    - **Cross-Platform**: Works across different ML frameworks
+    
+    ## Supported Formats
+    - **PyTorch**: `.pt`, `.pth` files containing model weights
+    - **TensorFlow**: `.h5` files or SavedModel directories (as zip)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            framework_dropdown = gr.Dropdown(
+                choices=["PyTorch", "TensorFlow"],
+                label="🔧 Select Framework",
+                info="Choose the framework your model was trained with",
+                value="PyTorch"
+            )
+            
+            model_upload = gr.File(
+                label="📁 Upload Model File",
+                file_types=[".pt", ".pth", ".h5", ".zip"],
+                info="Upload your model file (.pt/.pth for PyTorch, .h5 for TensorFlow)"
+            )
+            
+            convert_btn = gr.Button(
+                "🚀 Convert to SafeTensors",
+                variant="primary",
+                size="lg"
+            )
+        
+        with gr.Column():
+            output_file = gr.File(
+                label="💾 Download SafeTensors File",
+                info="Your converted model will appear here"
+            )
+            
+            gr.Markdown("""
+            ### 📋 Usage Instructions
+            1. **Select Framework**: Choose PyTorch or TensorFlow
+            2. **Upload Model**: Select your model file from your computer
+            3. **Convert**: Click the convert button
+            4. **Download**: Get your secure SafeTensors file
+            
+            ### ⚠️ Important Notes
+            - Only model weights are converted (no training code)
+            - Original model architecture code is still needed for inference
+            - Conversion preserves all tensor data and metadata
+            """)
+    
+    # Set up the conversion event
+    convert_btn.click(
+        fn=convert_to_safetensors,
+        inputs=[framework_dropdown, model_upload],
+        outputs=output_file,
+        show_progress=True
+    )
+    
+    gr.Markdown("""
+    ---
+    
+    ### 🛡️ Security Benefits
+    SafeTensors format eliminates security risks associated with pickle-based model formats by:
+    - Storing only tensor data (no executable code)
+    - Using a simple, well-defined file format
+    - Enabling safe model sharing and deployment
+    
+    ### 🔗 Learn More
+    - [SafeTensors Documentation](https://huggingface.co/docs/safetensors)
+    - [Hugging Face Model Hub](https://huggingface.co/models)
+    """)
+
+# For Hugging Face Spaces deployment
+if __name__ == "__main__":
+    iface.launch()