Update app.py

app.py

@@ -28,7 +28,7 @@ def get_oauth_info(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken |
         return print(f'{profile.username}: {org_names}')
 
 
-def compile_model(model_name, sram_size, tensor_size, optimize, model_loc, clock):
+def compile_model(model_name, vmem_value, lpmem_value):
 
     if oauth_info['token'] is None:
         return "ERROR - please log into HuggingFace to continue"
@@ -36,30 +36,31 @@ def compile_model(model_name, sram_size, tensor_size, optimize, model_loc, clock
     # Create a temporary directory
     with tempfile.TemporaryDirectory() as out_dir:
         print(f"Created temporary directory: {out_dir}")
+
+        vmem_size_limit = int(vmem_value * 1024)
+        lpmem_size_limit = int(lpmem_value * 1024)
         
         # Run the model fitter
-        results = sr100_model_compiler.sr100_model_compiler(
+        sucess, results = sr100_model_compiler.sr100_model_optimizer(
             model_file=model_name,
-            output_dir=f"{out_dir}",
-            model_loc=model_loc,
-            optimize=optimize,
-            arena_cache_size=int(float(tensor_size)*1.0e6)
+            vmem_size_limit=vmem_size_limit,
+            lpmem_size_limit=lpmem_size_limit
         )
         print(results)
 
     # Analyze the model
-    default_config = sr100_model_compiler.sr100_default_config()
+    #default_config = sr100_model_compiler.sr100_default_config()
 
-    default_config['sram_size'] = int(float(sram_size)*1.0e6)
-    default_config['core_clock'] = int(float(clock)*1.0e6)
-    success, perf_data = sr100_model_compiler.sr100_check_model(results=results, config=default_config)
+    #default_config['sram_size'] = int(float(sram_size)*1.0e6)
+    #default_config['core_clock'] = int(float(clock)*1.0e6)
+    #success, perf_data = sr100_model_compiler.sr100_check_model(results=results, config=default_config)
 
     output_text = ''
     if success:
         output_text = 'SUCCESS, model fits on SR100'
     else:
         output_text = 'FAILULRE model does not fit on SR100'
-    for key, value in perf_data.items():
+    for key, value in results.items():
         output_text += f'<br>{key} = {value}'
     
 #    try:
@@ -74,12 +75,6 @@ def compile_model(model_name, sram_size, tensor_size, optimize, model_loc, clock
 # Get all available models
 model_choices = glob.glob('models/*.tflite')
     
-def update_sliders(sram_slider_value, tensor_slider_value):
-
-    if tensor_slider_value >= sram_slider_value:
-        tensor_slider_value = sram_slider_value-0.1
-    return gr.update(value=tensor_slider_value)    
-
 with gr.Blocks() as demo:
     gr.LoginButton()
     text1 = gr.Markdown("SR100 Model Compiler - Compile a tflite model to SR100")
@@ -87,11 +82,8 @@ with gr.Blocks() as demo:
 
     # Setup model inputs
     with gr.Row():
-        sram_slider = gr.Slider(minimum=0, maximum=3, step=0.1, label="Set total SRAM size available in MB", value=3)
-        tensor_slider = gr.Slider(minimum=0, maximum=3, step=0.1, label="Set the SRAM size for tensor calculations in MB", value=1.5)
-        clock_slider = gr.Slider(minimum=100, maximum=400, step=10, label="Sets core clock frequeny (MHz)", value=400)
-    optimize = gr.Radio(choices=["Performance", "Size"], value='Performance', label='Performance model')
-    model_loc = gr.Radio(choices=["sram", "flash"], value="sram", label='Model weights target')
+        vmem_slider = gr.Slider(minimum=0, maximum=1.5, step=0.1, label="Set total VMEM SRAM size available in MiB", value=1.5)
+        lpmem_slider = gr.Slider(minimum=0, maximum=1.5, step=0.1, label="Set total LPMEM SRAM size in MiB", value=1.5)
     
     # Setup model compile
     model_dropdown = gr.Dropdown(
@@ -105,8 +97,7 @@ with gr.Blocks() as demo:
     compile_text = gr.Markdown("Waiting for model results")
     
     # Compute options
-    compile_btn.click(compile_model, inputs=[model_dropdown, sram_slider, tensor_slider, optimize, model_loc, clock_slider], outputs=[compile_text])
-    sram_slider.change(fn=update_sliders, inputs=[sram_slider, tensor_slider], outputs=tensor_slider)
+    compile_btn.click(compile_model, inputs=[model_dropdown, vmem_slider, lpmem_slider], outputs=[compile_text])
     demo.load(get_oauth_info, inputs=None, outputs=user_text)
 
 if __name__ == "__main__":