deve

app.py

@@ -244,8 +244,7 @@ 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 = AutoModelForSeq2SeqLM.from_pretrained('shorecode/t5-efficient-tiny-nh8-summarizer', num_labels=2)
+def predict(text, model):
     tokenizer = AutoTokenizer.from_pretrained('shorecode/t5-efficient-tiny-nh8-summarizer')
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(inputs)
@@ -274,7 +273,7 @@ def run_train(text):
     )
     #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)
+    result = predict(text, model)
     return result
 # Create Gradio interface
 try: