Update app.py

app.py

@@ -4,38 +4,10 @@ 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()
+from typing import Dict, List, Union
 
-user_input = st.text_area('Enter Text to Analyze')
-button = st.button("Analyze")
+from google.cloud import aiplatform
+from google.protobuf import json_format
+from google.protobuf.struct_pb2 import Value
 
-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))