Create app.py

app.py

@@ -0,0 +1,247 @@
+import os
+import json
+from pathlib import Path
+from typing import Annotated
+from autogen import AssistantAgent, UserProxyAgent
+from autogen.coding import LocalCommandLineCodeExecutor
+import gradio as gr
+from autogen import ConversableAgent
+from autogen import register_function
+import mysql.connector
+import random
+import requests
+from groq import Groq
+from dotenv import load_dotenv
+
+tool_resp = ""
+
+js = """
+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+
+    var text = '部門収益分析';
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                var randomColor = "#" + Math.floor(Math.random() * 16777215).toString(16);
+                letter.style.color = randomColor;
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.5s';
+                letter.innerText = text[i];
+
+                container.appendChild(letter);
+
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+
+                // Blink the text 3 times
+                for (var j = 0; j < 3; j++) {
+                    setTimeout(function() {
+                        letter.style.opacity = '0';
+                    }, 500 + j * 1000);
+                    setTimeout(function() {
+                        letter.style.opacity = '1';
+                    }, 1000 + j * 1000);
+                }
+            }, i * 250);
+        })(i);
+    }
+
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+
+    return 'Animation created';
+}
+"""
+
+load_dotenv(verbose=True)
+
+conn = mysql.connector.connect(
+        host=os.environ.get("HOST"),
+        user=os.environ.get("USER_NAME"),
+        password=os.environ.get("PASSWORD"),
+        port=os.environ.get("PORT"),
+        database=os.environ.get("DB"),
+        ssl_disabled=True
+    )
+
+cursor = conn.cursor(dictionary=True)
+
+def get_rounrobin():
+    select_one_data_query = "select api from agentic_apis_count order by counts ASC"
+    cursor.execute(select_one_data_query)
+    result = cursor.fetchall()
+    first_api = result[0]['api']
+    return first_api
+    
+# MySQLに接続
+def get_api_keys():
+    token = get_rounrobin()
+    os.environ["GROQ_API_KEY"] = token
+    
+    return token
+
+# Configure Groq
+config_list = [{
+    "model": "llama-3.3-70b-versatile",
+    "api_key": os.environ["GROQ_API_KEY"],
+    "api_type": "groq"
+}]
+
+
+# Create a directory to store code files from code executor
+work_dir = Path("coding")
+work_dir.mkdir(exist_ok=True)
+code_executor = LocalCommandLineCodeExecutor(work_dir=work_dir)
+
+# Define revenue tool
+#def get_current_revenue(location, unit="yen"):
+def get_current_revenue(location):
+    """Get the revenue for some location"""
+    data = requests.get('https://www.ryhintl.com/dbjson/getjson?sqlcmd=select `title` as country,`snippet` as revenue from cohere_documents_auto')
+    # 元のデータ
+    data = json.loads(data.content)
+
+    # 指定された形式に変換
+    revenue_data = {item["country"]: {"revenue": item["revenue"]} for item in data}
+    #print("revenue data:",revenue_data)
+    tmp = json.dumps({
+            "location": location.title(),
+            "revenue": revenue_data[location]["revenue"],
+            "unit": ""
+            #"unit": unit
+        })
+    #print("tmp:",tmp)
+
+    return json.dumps({
+            "location": location.title(),
+            "revenue": revenue_data[location]["revenue"],
+            "unit": ""
+        })
+
+        
+    #return json.dumps({"location": location, "revenue": "unknown"})
+
+# Create an AI assistant that uses the kpi tool
+assistant = AssistantAgent(
+#assistant = ConversableAgent(
+    name="groq_assistant",
+    system_message="""あなたは、次のことができる役に立つAIアシスタントです。
+    - 情報検索ツールを使用する
+    - 結果を分析して自然言語のみで説明する""",
+    llm_config={"config_list": config_list}
+)
+
+# Create a user proxy agent that only handles code execution
+user_proxy = UserProxyAgent(
+#user_proxy = ConversableAgent(
+    name="user_proxy",
+    human_input_mode="NEVER",
+    code_execution_config={"work_dir":"coding", "use_docker":False},
+    max_consecutive_auto_reply=2,
+    #llm_config={"config_list": config_list}
+)
+
+'''user_proxy.register_function(
+    function_map={
+        "get_current_revenue": get_current_revenue
+    }
+)'''
+
+
+
+
+# Register weather tool with the assistant
+@user_proxy.register_for_execution()
+@assistant.register_for_llm(description="snippetの内容")
+#@user_proxy.register_for_llm(description="Weather forecast for cities.")
+def revenue_analysis(
+    location: Annotated[str, "title"]
+    #unit: Annotated[str, "Revenue unit (dollar/yen)"] = "yen"
+) -> str:
+    #revenue_details = get_current_revenue(location=location, unit=unit)
+    revenue_details = get_current_revenue(location=location)
+    revenues = json.loads(revenue_details)
+    #print("resp:",f"{revenues['location']}の内容は{revenues['revenue']}")
+    global tool_resp
+    tool_resp = tool_resp + f"\n\n{location}\n{revenues['location']}の内容は{revenues['revenue']}"
+    
+    return f"{revenues['location']}の内容は{revenues['revenue']}"
+
+def get_revenue_and_plot(div1, div2, div3):
+    get_api_keys()
+
+    # Start the conversation
+    resp = user_proxy.initiate_chat(
+        assistant,
+        message=f"""3つのことをやってみましょう:
+        1. {div1}、{div2}、{div3}の内容をtoolを利用して抽出します。
+        2. toolを利用して抽出された内容を詳しく分析します。
+        3. 日本語で説明してください。
+        """
+    )
+    
+    total_tokens = resp.cost['usage_including_cached_inference']['llama-3.3-70b-versatile']['total_tokens']
+
+    #update counts
+    select_one_data_query = "SELECT counts FROM agentic_apis_count where api = '"+os.environ["GROQ_API_KEY"]+"'"
+    cursor.execute(select_one_data_query)
+    ext_key = cursor.fetchall()
+    key = [item['counts'] for item in ext_key]
+    calculated = key[0]+total_tokens/10000
+    
+    update_counts_query = "UPDATE agentic_apis_count SET counts = "+str(calculated)+" WHERE api = '"+os.environ["GROQ_API_KEY"]+"'"
+    
+    cursor.execute(update_counts_query)
+    conn.commit()
+    
+    groq_assistant_contents = [entry['content'] for entry in resp.chat_history if entry['role'] == 'user' and entry['name'] == 'groq_assistant']
+
+    global tool_resp
+    client = Groq(api_key=os.environ["GROQ_API_KEY"])
+    system_prompt = {
+        "role": "system",
+        "content": "You are a helpful assistant, answer questions concisely."
+    }
+
+    # Set the user prompt
+    user_input = tool_resp+"を要約してください。"
+    user_prompt = {
+        "role": "user", "content": user_input
+    }
+
+    # Initialize the chat history
+    chat_history = [system_prompt, user_prompt]
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=chat_history,
+        max_tokens=1024,
+        temperature=0)
+
+    kekka = response.choices[0].message.content
+
+    usages = "使用トークン数: "+str(total_tokens)+ " \n"+kekka
+    return groq_assistant_contents,usages
+
+# Create Gradio interface
+iface = gr.Interface(
+    js=js,
+    fn=get_revenue_and_plot,
+    inputs=[gr.Dropdown(choices=["上期経営会議議事録", "セキュリティー会議資料", "コーポレートガバナンス会議資料"], label="上期経営会議議事録",value="上期経営会議議事録"), gr.Dropdown(choices=["上期経営会議議事録", "セキュリティー会議資料", "コーポレートガバナンス会議資料"], label="セキュリティー会議資料",value="セキュリティー会議資料"), gr.Dropdown(choices=["上期経営会議議事録", "セキュリティー会議資料", "コーポレートガバナンス会議資料"], label="コーポレートガバナンス会議資料",value="コーポレートガバナンス会議資料")],
+    outputs=[gr.Textbox(label="結果"),gr.Textbox(label="Usageデータとツール結果")],
+    title="資料の分析",
+    description="プロンプトを入力してデータを取得し、内容を分析します。",
+    submit_btn="実行",
+    clear_btn="クリア",
+    flagging_mode="never"
+)
+
+iface.launch()