Update app.py

app.py

@@ -1,34 +1,3 @@
 import gradio as gr
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
-#checkpoint = "bigscience/bloomz" # english
-checkpoint = "bigscience/bloomz" # english
-#checkpoint = "bigscience/bloomz-7b1-mt" # non english
-
-import torch
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
-#tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-#model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=False).to(device)
-
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint)
-
-def get_result(prompt):
-    # prompt = f"'''{str(prompt)}'''"
-    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
-    outputs = model.generate(inputs, max_length= len(prompt)+1000)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-title = "Bloomz (english small)"
-description = "Write an instruction and get the Bloomz result."
-examples = [["Translate to English: Je t'aime."]]
-
-demo = gr.Interface(fn=get_result, inputs="text", outputs="text",
-                    title=title,
-                    description=description,
-                    examples=examples,
-                    allow_flagging="never")
-
-demo.launch()
+gr.Interface.load(("huggingface/bigscience/bloomz"), allow_flagging=True, enable_queue=True, api_key="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").launch()