Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+
+# ここを Llama / Mistral など好きなモデルに変更
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2"
+# MODEL_NAME = "meta-llama/Llama-3.1-8B-Instruct"  # ← Llama に変更したい場合
+
+# モデルとトークナイザのロード
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def chat_fn(message, history):
+    # 過去履歴を LLM のプロンプト形式に変換
+    prompt = ""
+    for user, assistant in history:
+        prompt += f"<s>[ユーザー]: {user}\n[アシスタント]: {assistant}</s>\n"
+    prompt += f"<s>[ユーザー]: {message}\n[アシスタント]:"
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.9
+    )
+
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # 最後のアシスタント発言だけ抽出
+    if "[アシスタント]:" in response:
+        response = response.split("[アシスタント]:")[-1].strip()
+
+    history.append((message, response))
+    return response, history
+
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🦙💬 Simple Llama / Mistral Chatbot")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Message")
+
+    def user_send(user_message, chat_history):
+        return "", chat_history + [[user_message, None]]
+
+    msg.submit(user_send, [msg, chatbot], [msg, chatbot]).then(
+        chat_fn, [msg, chatbot], [chatbot]
+    )
+
+demo.launch()