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# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
import streamlit as st
import numpy as np
import torch
@st.cache_resource
def get_model():
tokenizer = AutoTokenizer.from_pretrained("onurnsfw/Gemma2-9b-classifier")
model = AutoModelForCausalLM.from_pretrained("onurnsfw/Gemma2-9b-classifier")
return tokenizer,model
tokenizer,model = get_model()
user_input = st.text_area('Enter Text to Analyze')
button = st.button("Analyze")
if user_input and button :
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
inputs = tokenizer(
[
alpaca_prompt.format(
"Match the potential use case with the corresponding activity and emission values based on the provided context.", # instruction
"{user_input}",
"",
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
st.write("Prediction: ",tokenizer.batch_decode(outputs))
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