app.py

import gradio as gr
from gradio import ChatMessage
#from crewai.tools import tool, BaseTool
from groq import Groq
import requests
import uvicorn
import os
import mysql.connector
from qdrant_client import QdrantClient

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent

# MySQLに接続
conn = mysql.connector.connect(
    host="www.ryhintl.com",
    user="smairuser",
    password="smairuser",
    port=36000,
    database="smair"
)

# カーソルを取得
cursor = conn.cursor(dictionary=True)

# List API Keys
select_one_data_query = "SELECT * FROM agentic_apis"
cursor.execute(select_one_data_query)
result = cursor.fetchall()
# JSONをパースしてkeyを抽出
keys = [item['key'] for item in result]

os.environ["GROQ_API_KEY"] = keys[2]
os.environ["COHERE_API_KEY"] = keys[3]

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 = '人工知能（AI）の現状と未来';
    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);
            }, i * 250);
        })(i);
    }

    var gradioContainer = document.querySelector('.gradio-container');
    gradioContainer.insertBefore(container, gradioContainer.firstChild);

    return 'Animation created';
}
"""

sel_method = "COHERE"

def on_change(selected_option):
    global sel_method
    sel_method = selected_option
    return f"選択されたオプション: {selected_option}"

async def autogen(qry: str):
    # Prompts
    userPrompt=qry
    assistant="あなたはAI会社のアナリストです。"
    ragproxyagent="あなたは高度な知識を持つAI会社の上席コンサルタントです。assistantによる応答を分析し、詳細情報を提供します。"

    global sel_method
    
    if sel_method == "COHERE":
        # Cohere config
        config_list = [
            {
                "model": "command-r-plus-08-2024",
                "api_key": os.environ["COHERE_API_KEY"],
                "api_type": "cohere",
            }
        ]
    else:
        # GROQ config
        config_list = [
            {
                "model": "llama-3.3-70b-versatile",
                "api_key": os.environ["GROQ_API_KEY"],
                "api_type": "groq",
            }
        ]

    assistant = AssistantAgent(
    name="assistant",
    system_message="You are a helpful assistant.",
    llm_config={
        "timeout": 600,
        "cache_seed": 42,
        "config_list": config_list,
    },
    )

    client = QdrantClient(url="https://02cbe366-829e-43a6-adf5-3b712a886c21.us-west-1-0.aws.cloud.qdrant.io", api_key="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.hWu5_qKaYHUhuMAjUScqw1R_1kkXiidv337wuGKcu9o")
    # Create user proxy agent RetrieveUserProxyAgent(
    ragproxyagent = RetrieveUserProxyAgent(
    name="ragproxyagent",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,
    retrieve_config = {
        "task": "qa",
        "docs_path": [
            "https://www.ryhintl.com/ai-predict.md"
        ],
        "chunk_token_size": 2000,
        "model": config_list[0]["model"],
        "vector_db": "qdrant",  # Qdrant Cloud database
        "collection_name": "ai_predict",
        #"db_config": {
            #"connection_string": "https://02cbe366-829e-43a6-adf5-3b712a886c21.us-west-1-0.aws.cloud.qdrant.io",  # Qdrant Cloud connection string
            #"api_key": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.hWu5_qKaYHUhuMAjUScqw1R_1kkXiidv337wuGKcu9o",  # Qdrant Cloud API key
            #"database_name": "human_database",  # Qdrant Cloud database (if applicable)
            #"index_name": "scout_index",
            #"wait_until_index_ready": 120.0,  # Setting to wait 120 seconds or until index is constructed before querying
            #"wait_until_document_ready": 120.0,  # Setting to wait 120 seconds or until document is properly indexed after insertion/update
        #},
        "db_config": {"client": client},
        "get_or_create": True,  # set to False if you don't want to reuse an existing collection
        "overwrite": True,  # set to True if you want to overwrite an existing collection, each overwrite will force a index creation and reupload of documents
        },
        code_execution_config=False,  # set to False if you don't want to execute the code
    )


    # Create a group chat between all agents
    groupChat = GroupChat(
        agents=[assistant, ragproxyagent],
        messages=[],
        max_round=3,
    )

    groupChatManager = GroupChatManager(
        groupchat=groupChat,
        llm_config={"config_list": config_list}
    )

    # Initiate the chat using the User Proxy Agent and pass the user prompt
    #chat_result = ragproxyagent.initiate_chat(
        #groupChatManager,
        #message=userPrompt
    #)
    chat_result = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=userPrompt)
    #user_contents = [entry['content'] for entry in chat_result['chat_history'] if entry['role'] == 'user']
    
    #chat_result = ragproxyagent.initiate_chat(assistant, message=userPrompt)
    #print(chat_result)

    chat_result_dict = vars(chat_result)
    user_contents = [entry['content'] for entry in chat_result_dict['chat_history'] if entry['role'] == 'user']
    #print("user_contents")
    #print(user_contents)
    #print("\n\n")
    
    #print(user_contents[0])
    #return user_contents[0]

    basic_content = [entry["content"] for entry in chat_result_dict["chat_history"] if entry["role"] == "assistant"]
    #print("basic_content")
    #print(basic_content)
    #return basic_content
    return user_contents[0]+"\n\n"+basic_content[0]

    
    #user_contents = [entry['content'] for entry in chat_result['chat_history'] if entry['role'] == 'user']

    

    #basic_content = [entry["content"] for entry in chat_result_dict["chat_history"] if entry["name"] == "Basic_Assistant_Agent"]
    #professional_content = [entry["content"] for entry in chat_result_dict["chat_history"] if entry["name"] == "Professional_Assistant_Agent"]

    #professional_content = [entry['content'] for entry in chat_result['chat_history'] if entry['name'] == 'Professional_Assistant_Agent']
    #summary = chat_result['summary']
    
    #return chat_result
    #comments = "Basic Assitant: \n"+str(basic_content[0])+"\n\nProfessional Assistant: \n"+str(professional_content[0])
    #return comments
    #return professional_content[0]

def process_rag(prompt):

    if prompt == "":
        return "プロンプトを入力してください。", "プロンプトは必須です。"
    else:
        url = 'http://www.ryhintl.com/eprag-be/llm?query='+prompt
        res = requests.get(url)
        rtn = res.content.decode('utf-8')
    
        return rtn


def process_eprag(prompt):
    
    if prompt == "":
        return "プロンプトを入力してください。", "プロンプトは必須です。"
    else:
        client = Groq(api_key=os.environ["GROQ_API_KEY"])

        # Set the system prompt
        system_prompt = {
            "role": "system",
            "content": "あなたは便利なアシスタントです。質問には簡潔に答えてください。"
        }

        # Set the user prompt
        user_input = prompt
        user_prompt = {
            "role": "user", "content": user_input
        }

        # Initialize the chat history
        chat_history = [system_prompt, user_prompt]

        response = client.chat.completions.create(
            #model="llama3-70b-8192",
            model="llama-3.3-70b-versatile",
            messages=chat_history,
            max_tokens=1024,
            temperature=0)

        return response.choices[0].message.content  

with gr.Blocks(js=js,title="人材スカウトのガイドライン") as llm:
    
    with gr.Tab("LLM"):
        
        gr.Markdown("# 🗞️ LLM")
        gr.Text(label="人工知能（AI）の現状と未来",value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=3)
        
        with gr.Row():
            eprag_input = gr.Textbox(value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=5,label="プロンプト", type="text")
            
        with gr.Row():
            eprag_output = gr.Textbox(label="AIアシスタントの応答")
            
        submit_button = gr.Button("LLM プロセス", variant="primary")
        submit_button.click(
            process_eprag,
            inputs=[eprag_input],
            outputs=[eprag_output]
        )

    with gr.Tab("RAG"):
        
        gr.Markdown("# 🗞️ RAG")
        gr.Text(label="人工知能（AI）の現状と未来",value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=3)
        
        with gr.Row():
            rag_input = gr.Textbox(value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=5,label="プロンプト", type="text")
            
        with gr.Row():
            rag_output = gr.Textbox(label="AIアシスタントの応答")
            
        submit_button = gr.Button("RAG プロセス", variant="primary")
        submit_button.click(
            process_rag,
            inputs=[rag_input],
            outputs=[rag_output]
        )

    with gr.Tab("AGENT AUTOGEN"):
        gr.Markdown("# 🗞️ AGENTIC RAG AUTOGEN")
        gr.Text(label="人工知能（AI）の現状と未来",value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=3)
        
        with gr.Row():
            auto_input = gr.Textbox(value="AIが未来にどうなるか心配しています。人工知能（AI）を中心とする ICT の進化はどうなるかについて教えてください。",lines=5,label="プロンプト", type="text")
            
        with gr.Row():
            method = gr.Dropdown(
                ["COHERE", "GROQ"], label="LLM", info="LLMを選んでください"
            )
            selected_method = gr.Text(label="Method", visible=False)
            method.change(on_change, inputs=method, outputs=selected_method)
            
        with gr.Row():
            auto_output = gr.Textbox(label="AIアシスタントの応答")
       
        submit_button = gr.Button("AUTOGEN プロセス", variant="primary")
        submit_button.click(
            autogen,
            inputs=[auto_input],
            outputs=[auto_output]
        )

if __name__ == "__main__":
    llm.launch()