Create app.py

app.py

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+import gradio as gr
+import torch
+import coremltools as ct
+from huggingface_hub import snapshot_download
+from transformers import AutoModel, AutoTokenizer
+import os
+import tempfile
+import shutil
+
+def convert_to_coreml(model_name, input_length=128, ios_version="iOS15"):
+    
+    try:
+        print(f"Downloading model {model_name}...")
+        # Download model from Huggingface
+        model_path = snapshot_download(repo_id=model_name)
+        
+        print("Loading model...")
+        # Load model and tokenizer
+        model = AutoModel.from_pretrained(model_path)
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        
+        # Set model to evaluation mode
+        model.eval()
+        
+        print("Creating example input...")
+        # Create example input
+        if hasattr(tokenizer, "pad_token") and tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+            
+        sample_text = "This is a sample input for conversion"
+        inputs = tokenizer(sample_text, return_tensors="pt", padding="max_length", max_length=input_length)
+        
+        print("Tracing model...")
+        # Trace the model
+        traced_model = torch.jit.trace(model, [inputs["input_ids"], inputs["attention_mask"]])
+        
+        # Define Core ML target version
+        if ios_version == "iOS15":
+            target = ct.target.iOS15
+        elif ios_version == "iOS16":
+            target = ct.target.iOS16
+        else:
+            target = ct.target.iOS17
+        
+        print(f"Converting to Core ML (targeting {ios_version})...")
+        # Convert to Core ML
+        mlmodel = ct.convert(
+            traced_model,
+            inputs=[
+                ct.TensorType(name="input_ids", shape=inputs["input_ids"].shape),
+                ct.TensorType(name="attention_mask", shape=inputs["attention_mask"].shape)
+            ],
+            minimum_deployment_target=target
+        )
+        
+        # Create a temporary directory to save the model
+        temp_dir = tempfile.mkdtemp()
+        model_file = os.path.join(temp_dir, f"{model_name.split('/')[-1]}.mlmodel")
+        
+        print(f"Saving model to {model_file}...")
+        # Save the model
+        mlmodel.save(model_file)
+        
+        return model_file, "Conversion successful!"
+    
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+def process(model_name, input_length, ios_version):
+    model_file, message = convert_to_coreml(model_name, int(input_length), ios_version)
+    if model_file:
+        return message, model_file
+    else:
+        return message, None
+
+# Create Gradio interface
+with gr.Blocks(title="Huggingface to Core ML Converter") as demo:
+    gr.Markdown("# Huggingface to Core ML Model Converter")
+    gr.Markdown("Enter a Huggingface model ID and convert it to Core ML format.")
+    
+    with gr.Row():
+        with gr.Column():
+            model_name = gr.Textbox(label="Huggingface Model ID (e.g., 'your-username/your-model')")
+            input_length = gr.Slider(minimum=16, maximum=512, value=128, step=16, label="Input Length")
+            ios_version = gr.Dropdown(choices=["iOS15", "iOS16", "iOS17"], value="iOS15", label="Target iOS Version")
+            convert_button = gr.Button("Convert Model")
+        
+        with gr.Column():
+            output_message = gr.Textbox(label="Status")
+            file_output = gr.File(label="Converted Model")
+    
+    convert_button.click(
+        process,
+        inputs=[model_name, input_length, ios_version],
+        outputs=[output_message, file_output]
+    )
+
+demo.launch()