Create app.py

app.py

@@ -0,0 +1,41 @@
+# 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))