Update app.py

app.py

@@ -1,13 +1,49 @@
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import streamlit as st
-import numpy as np
+from unsloth import FastLanguageModel
 import torch
+import streamlit as st
+from transformers import TextStreamer
+
+
+@st.cache_resource
+def load_model():
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "lora_model", 
+        max_seq_length = 2048,
+        dtype = None,
+        load_in_4bit = True,
+    )
+    FastLanguageModel.for_inference(model)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+st.title("Activity and Emission Prediction")
+st.write("Match the potential use case with the corresponding activity and emission values based on provided context.")
+
+
+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:
+{}"""
 
+instruction = st.text_input("Instruction", "Match the potential use case with the corresponding activity and emission values based on the provided context.")
+input_text = st.text_area("Input", "Doğal Gaz Kullanımı, Gaz Faturası Yönetimi, Isınma Maliyetleri, Enerji Tasarrufu, Gaz Dağıtımı")
 
-from typing import Dict, List, Union
+# Button to trigger model generation
+if st.button("Generate Response"):
+    with st.spinner("Generating response..."):
+        # Prepare inputs for the model
+        formatted_prompt = alpaca_prompt.format(instruction, input_text, "")
+        inputs = tokenizer([formatted_prompt], return_tensors="pt").to("cuda")
 
-from google.cloud import aiplatform
-from google.protobuf import json_format
-from google.protobuf.struct_pb2 import Value
+        outputs = model.generate(**inputs, max_new_tokens=128)
+        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+        st.write("### Response")
+        st.write(response_text)