Create app.py

app.py

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+import streamlit as st
+import os
+os.system("pip install torch transformers sentencepiece accelerate")
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# モデルとトークナイザの初期化
+model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-1b", device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-1b")
+
+# 推論用の関数
+def generate_text(input_text, max_new_tokens, temperature, top_p, repetition_penalty):
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        tokens = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=repetition_penalty,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    output = tokenizer.decode(tokens[0], skip_special_tokens=True)
+    return output
+
+# Streamlitアプリの設定
+st.title("Causal Language Modeling")
+st.write("AIによる文章生成")
+
+# パラメータの入力
+input_text = st.text_area("入力テキスト")
+max_new_tokens = st.slider("生成する最大トークン数", min_value=1, max_value=512, value=64)
+temperature = st.slider("Temperature", min_value=0.1, max_value=2.0, value=0.7)
+top_p = st.slider("Top-p", min_value=0.1, max_value=1.0, value=0.9)
+repetition_penalty = st.slider("Repetition Penalty", min_value=0.1, max_value=2.0, value=1.05)
+
+# 推論結果の表示
+if st.button("生成"):
+    output = generate_text(input_text, max_new_tokens, temperature, top_p, repetition_penalty)
+    st.write("生成されたテキスト:")
+    st.write(output)