deve

app.py

@@ -244,7 +244,11 @@ def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size
     return 'DONE!'#train_result
 
 # Define Gradio interface
-def predict(text, model):
+@spaces.GPU
+def predict(text):
+    config = AutoConfig.from_pretrained("shorecode/t5-efficient-tiny-nh8-summarizer")
+    model = AutoModelForSeq2SeqLM.from_config(config)
+    initialize_weights(model)
     tokenizer = AutoTokenizer.from_pretrained('shorecode/t5-efficient-tiny-nh8-summarizer')
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(inputs)
@@ -272,8 +276,7 @@ def run_train(text):
         bias="none"  # Bias handling
     )
     #model = get_peft_model(model, lora_config)
-    #result = fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad)
-    result = predict(text, model)
+    result = fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad)    
     return result
 # Create Gradio interface
 try:
@@ -295,7 +298,7 @@ try:
     )
     '''
     iface = gr.Interface(
-        fn=run_train,
+        fn=predict,
         inputs=[
             gr.Textbox(label="Query"),
         ],