Update app.py

app.py

@@ -1,21 +1,21 @@
 import streamlit as st
 from transformers import pipeline
-from PIL import Image
+
 # TO-DD: ??? 부분의 코드를 완성하시오
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+pipeline = AutoModelForCausalLM.from_pretrained(task="translation", model="maywell/Synatra-7B-v0.3-Translation", tokenizer="maywell/Synatra-7B-v0.3-Translation")
+
+device = "cuda" # the device to load the model onto
 
-st.title("Hot Dog? Or Not?")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+messages = [
+    {"role": "user", "content": "바나나는 원래 하얀색이야?"},
+]
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-# TO-DO: ??? 부분의 코드를 완성하시오
-    predictions = pipeline(image)
+model_inputs = encodeds.to(device)
+model.to(device)
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])