Update app.py

app.py

@@ -1,11 +1,25 @@
 import gradio as gr
-from transformers import pipeline
 
-pipeline = pipeline(task="feedback-distilbert", model="isimorfizam/distilbert-base-cased-distilled-squad-feedback")
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+base_model = "google/gemma-2b-it"
+adapter_model = "isimorfizam/logs"
+
+model = AutoModelForCausalLM.from_pretrained(base_model)
+model = PeftModel.from_pretrained(model, adapter_model)
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+model = model.to("cuda")
 
 def predict(question):
-    question_answerer = pipeline("question-answering", model=MODEL_CHECKPOINT)
-    return question_answerer(question=question,context=context)
+    
+    #input_pretext = 'Answer the following question for me.'
+    input_text = question
+    input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+    
+    outputs = model.generate(**input_ids, max_length = 100)
+    return tokenizer.decode(outputs[0])
 
 
 gradio_app = gr.Interface(