Update app.py

app.py

@@ -1,27 +1,4 @@
-import gradio as gr
-import peft
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-# Load the model and config when the script starts
-config = PeftConfig.from_pretrained("PhantHive/bigbrain")
-model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
-model = PeftModel.from_pretrained(model, "PhantHive/bigbrain")
-
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
-
-
-def greet(text):
-    batch = tokenizer(f"'{text}' ->: ", return_tensors='pt')
-
-    # Use torch.no_grad to disable gradient calculation
-    with torch.no_grad():
-        output_tokens = model.generate(**batch, do_sample=True, max_new_tokens=50)
-
-    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+pipe = pipeline("text-generation", model="PhantHive/bigbrain")