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AlpacaGuanacoJaGPT1B

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OzoneAsai commited on 25 days ago

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1 Parent(s): e710fbe

Create app.py

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app.py +184 -0

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from flask import Flask, request, jsonify, render_template_string, Response
+import time
+from flask_sse import sse
+import redis
+# Flaskアプリケーションの設定
+app = Flask(__name__)
+app.config["REDIS_URL"] = "redis://localhost:6379/0"
+app.register_blueprint(sse, url_prefix='/stream')
+# デバイスの設定
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# トークナイザーとモデルの読み込み
+tokenizer = AutoTokenizer.from_pretrained("inu-ai/alpaca-guanaco-japanese-gpt-1b", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("inu-ai/alpaca-guanaco-japanese-gpt-1b").to(device)
+# 定数
+MAX_ASSISTANT_LENGTH = 100
+MAX_INPUT_LENGTH = 1024
+INPUT_PROMPT = r'<s>\n以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。\n[SEP]\n指示:\n{instruction}\n[SEP]\n入力:\n{input}\n[SEP]\n応答:\n'
+NO_INPUT_PROMPT = r'<s>\n以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n[SEP]\n指示:\n{instruction}\n[SEP]\n応答:\n'
+# HTMLテンプレート
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html lang="ja">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Chat Interface</title>
+    <style>
+        body { font-family: Arial, sans-serif; }
+        .container { max-width: 600px; margin: auto; padding: 20px; }
+        .chat-box { border: 1px solid #ccc; padding: 10px; height: 300px; overflow-y: scroll; }
+        .chat-entry { margin-bottom: 10px; }
+        .chat-entry.user { text-align: right; }
+        .input-group { display: flex; }
+        .input-group input { flex: 1; padding: 10px; border: 1px solid #ccc; border-radius: 4px; }
+        .input-group button { padding: 10px; border: none; background-color: #28a745; color: white; cursor: pointer; }
+        .input-group button:hover { background-color: #218838; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Chat Interface</h1>
+        <div class="chat-box" id="chat-box"></div>
+        <div class="input-group">
+            <input type="text" id="user-input" placeholder="質問を入力してください...">
+            <button onclick="sendMessage()">送信</button>
+        </div>
+    </div>
+    <script>
+        const chatBox = document.getElementById('chat-box');
+        const userInput = document.getElementById('user-input');
+        let conversationHistory = [];
+        function addMessageToChat(role, message) {
+            const entry = document.createElement('div');
+            entry.className = 'chat-entry ' + role;
+            entry.textContent = role === 'user' ? 'User: ' + message : 'Assistant: ' + message;
+            chatBox.appendChild(entry);
+            chatBox.scrollTop = chatBox.scrollHeight;
+        }
+        function sendMessage() {
+            const message = userInput.value.trim();
+            if (message === '') return;
+            addMessageToChat('user', message);
+            userInput.value = '';
+            fetch('/generate', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({
+                    role_instruction: [
+                        "User:あなたは「ずんだもん」なのだ。東北ずん子の武器である「ずんだアロー」に変身する妖精またはマスコットなのだ。一人称は「ボク」で語尾に「なのだ」を付けてしゃべるのだ。",
+                        "Assistant:了解したのだ！"
+                    ],
+                    conversation_history: conversationHistory,
+                    new_conversation: message
+                })
+            })
+            .then(response => response.json())
+            .then(data => {
+                const assistantMessage = data.response.split('Assistant:')[1].trim();
+                addMessageToChat('assistant', assistantMessage);
+                conversationHistory.push('User:' + message);
+                conversationHistory.push('Assistant:' + assistantMessage);
+            })
+            .catch(error => {
+                console.error('Error:', error);
+                alert('エラーが発生しました。コンソールを確認してください。');
+            });
+        }
+        // SSEの設定
+        const eventSource = new EventSource("/stream");
+        eventSource.onmessage = function(event) {
+            const message = event.data;
+            addMessageToChat('assistant', message);
+        };
+    </script>
+</body>
+</html>
+"""
+def prepare_input(role_instruction, conversation_history, new_conversation):
+    """入力テキストを整形する関数"""
+    instruction = "".join([f"{text}\n" for text in role_instruction])
+    instruction += "\n".join(conversation_history)
+    input_text = f"User:{new_conversation}"
+    return INPUT_PROMPT.format(instruction=instruction, input=input_text)
+def format_output(output):
+    """生成された出力を整形する関数"""
+    return output.lstrip("<s>").rstrip("</s>").replace("[SEP]", "").replace("\\n", "\n")
+def trim_conversation_history(conversation_history, max_length):
+    """会話履歴を最大長に収めるために調整する関数"""
+    while len(conversation_history) > 2 and sum([len(tokenizer.encode(text, add_special_tokens=False)) for text in conversation_history]) + max_length > MAX_INPUT_LENGTH:
+        conversation_history.pop(0)
+        conversation_history.pop(0)
+    return conversation_history
+def generate_response(role_instruction, conversation_history, new_conversation):
+    """新しい会話に対する応答を生成する関数"""
+    conversation_history = trim_conversation_history(conversation_history, MAX_ASSISTANT_LENGTH)
+    input_text = prepare_input(role_instruction, conversation_history, new_conversation)
+    token_ids = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt")
+    with torch.no_grad():
+        output_ids = model.generate(
+            token_ids.to(model.device),
+            min_length=len(token_ids[0]),
+            max_length=min(MAX_INPUT_LENGTH, len(token_ids[0]) + MAX_ASSISTANT_LENGTH),
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+            bos_token_id=tokenizer.bos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            bad_words_ids=[[tokenizer.unk_token_id]]
+        )
+    output = tokenizer.decode(output_ids.tolist()[0])
+    formatted_output_all = format_output(output)
+    response = f"Assistant:{formatted_output_all.split('応答:')[-1].strip()}"
+    conversation_history.append(f"User:{new_conversation}".replace("\n", "\\n"))
+    conversation_history.append(response.replace("\n", "\\n"))
+    return formatted_output_all, response
+@app.route('/')
+def home():
+    """ホームページをレンダリング"""
+    return render_template_string(HTML_TEMPLATE)
+@app.route('/generate', methods=['POST'])
+def generate():
+    """Flaskエンドポイント: /generate"""
+    data = request.json
+    role_instruction = data.get('role_instruction', [])
+    conversation_history = data.get('conversation_history', [])
+    new_conversation = data.get('new_conversation', "")
+    if not role_instruction or not new_conversation:
+        return jsonify({"error": "role_instruction and new_conversation are required fields"}), 400
+    formatted_output_all, response = generate_response(role_instruction, conversation_history, new_conversation)
+    # ここでSSEを介してリアルタイムで応答をストリームします
+    for word in response.split():
+        sse.publish({"message": word}, type='message')
+        time.sleep(0.5)  # 送信間隔をシミュレート
+    return jsonify({"response": response, "conversation_history": conversation_history})
+if __name__ == '__main__':
+    app.run(debug=True, host="0.0.0.0", port=7860)