Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
 import spaces
-import training
+#import training
 from huggingface_hub import HfApi
 from huggingface_hub import whoami
 
-model = training.model_training()
+#model = training.model_training()
 
 # Get top-level authorizations
 oauth_info = {'username' : None, 'token' : None}
-api = None
+api = HfApi()
 
 def get_oauth_info(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None) -> str:
     global oauth_info
@@ -17,65 +17,43 @@ def get_oauth_info(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken |
     print(f'profile = {profile}')
     if profile is None:
         oauth_info['username'] = None
-        oauth_info['token'] = None
-        api = HfApi()
-        
+        oauth_info['token'] = None        
         return "Please login to the Huggingface with login button"
     else:
         #print(f'Testing {profile} for username')
         oauth_info['username'] = profile.username
         oauth_info['token'] = oauth_token.token
-
-        api = HfApi(token=oauth_token.token)
         org_names = [org["name"] for org in whoami(oauth_token.token)["orgs"]]
 
         return print(f'{profile.username}: {org_names}')
 
 
-def get_dataset():
-    dataset = 'Synaptics/COCO8'
-    return model.get_dataset(dataset, oauth_info['token'])
-
-def run_training(epochs_text, batch_text):
+def compile_model(model_name):
 
-    try:
-        epochs = int(epochs_text)
-        batch = int(batch_text)
-    except Exception as e:
-        epochs = 100
-        batch = 16
-        print(f'ERROR converting {epochs_text}, and {batch_text}')
+#    try:
+#        epochs = int(epochs_text)
+#        batch = int(batch_text)
+#    except Exception as e:
+#        epochs = 100
+#        batch = 16
+#        print(f'ERROR converting {epochs_text}, and {batch_text}')
 
     # Run the training
-    return model.run_training(epochs=epochs, batch=batch)
+#    return model.run_training(epochs=epochs, batch=batch)
 
-def quantize_model():
-    # Display message and run quantization
-    model.quantize()
-    return "Quantizing the trained model"
+    return f"Compiled model {model_name}"
 
 with gr.Blocks() as demo:
     gr.LoginButton()
     text1 = gr.Markdown("Starting to test SyNet")
     user_text = gr.Markdown("")
 
-    load_btn = gr.Button("Load dataset")
-    load_text = gr.Markdown("Click to load dataset")
-
-    with gr.Row():
-        epochs_text = gr.Textbox(label='Epochs', value='100')
-        batch_text = gr.Textbox(label='Batch', value='16')
-    with gr.Column():
-        train_btn = gr.Button("Train")
-        train_text = gr.Markdown("Click to train")
+    model_text = gr.Textbox(label='LiteRT Model', value='Synaptics/my_model')
 
-        quant_btn = gr.Button("Quantize")
-        quant_text = gr.Markdown("Click to quantize")
-    
-    load_btn.click(get_dataset, inputs=None, outputs=[load_text])
-    train_btn.click(run_training, inputs=[epochs_text, batch_text], outputs=[train_text])
-    quant_btn.click(quantize_model, inputs=None, outputs=[quant_text])
+    compile_btn = gr.Button("Compile Model")
+    compile_text = gr.Markdown("Click to load dataset")
 
+    compile_btn.click(compile_model, inputs=[model_text], outputs=[compile_text])
     demo.load(get_oauth_info, inputs=None, outputs=user_text)
 
 if __name__ == "__main__":