Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from transformers import AutoModel
+from safetensors.torch import load_file, save_file
+import torch
+import os
+
+# Function to download and modify model
+def add_fuzz_to_model(model_name, min_val, max_val):
+    # Download model weights from Hugging Face
+    model = AutoModel.from_pretrained(model_name, use_safetensors=True)
+    model_file = f"{model_name}.safetensors"
+    model.save_pretrained(".", safe_serialization=True)  # Save locally for access
+
+    # Load safetensors
+    weights = load_file(model_file)
+    
+    # Apply fuzz to each weight tensor
+    def add_fuzz(tensor, min_val, max_val):
+        noise = torch.empty_like(tensor).uniform_(min_val, max_val)
+        return tensor + noise
+
+    modified_weights = {}
+    for key, tensor in weights.items():
+        modified_weights[key] = add_fuzz(tensor, min_val, max_val)
+    
+    # Save modified weights
+    modified_file = f"{model_name}_fuzzed.safetensors"
+    save_file(modified_weights, modified_file)
+
+    return f"Model processed and saved as {modified_file}"
+
+# Gradio UI
+def fuzz_weights_ui():
+    with gr.Blocks() as interface:
+        gr.Markdown("### Add Fuzz to Hugging Face Model Weights")
+
+        with gr.Row():
+            model_name = gr.Textbox(label="Hugging Face Model Name", placeholder="e.g., gpt2")
+        
+        with gr.Row():
+            min_val = gr.Slider(-0.5, 0.0, value=-0.1, step=0.01, label="Minimum Fuzz")
+            max_val = gr.Slider(0.0, 0.5, value=0.1, step=0.01, label="Maximum Fuzz")
+        
+        with gr.Row():
+            process_button = gr.Button("Apply Fuzz")
+            result = gr.Textbox(label="Result")
+
+        # Define interaction
+        process_button.click(
+            fn=add_fuzz_to_model,
+            inputs=[model_name, min_val, max_val],
+            outputs=[result],
+        )
+
+    return interface
+
+# Launch the app
+interface = fuzz_weights_ui()
+interface.launch()