Update app.py

app.py

@@ -1,7 +1,40 @@
 import streamlit as st
-import numpy as np
-import transformers
+from utils import is_hiragana_or_katakana
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
+from st_keyup import st_keyup
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-st.write(transformers.__version__)
+# model_name_or_path = "tokyotech-llm/Llama-3-Swallow-8B-v0.1"
+model_name_or_path = "tokyotech-llm/Swallow-7b-hf"
+# model_name_or_path = "llm-jp/llm-jp-1.3b-v1.0"
+tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16)
+# model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+
+
+# Show title and description.
+st.title("丸点棒AI")
+st.write(
+    ""
+)
+
+query_candidates = {"": ([""], 0)}
+
+query = st_keyup(
+    "お題", 
+    placeholder="ひらがな/カタカナのみを入力",
+)
+
+if query != "" and is_hiragana_or_katakana(query):
+    st.info("OK")
+    # if query in query_candidates:
+    #     top_candidates, top_losses = query_candidates[query]
+    # else:
+    #     top_candidates, top_losses = search_candidates(query, top_k=100)
+    # answers = ["{}: {:.2f}".format(top_candidates[index], top_losses[index]) for index in range(min(len(top_candidates), 10))]
+    # value = "<br>".join(answers)
+    # value += f"<br>({len(top_candidates)}候補)"
+    # html.value = value
+else:
+    st.info("ひらがな/カタカナのみを入力してください")