app.py

import gradio as gr
from groq import Groq
from huggingface_hub import list_models
import os
from dotenv import load_dotenv
import requests
import json

load_dotenv(verbose=True)
sqlcmd = os.environ.get("SQLCMD_URL")
lresponse = requests.get(sqlcmd) 
loginfo= lresponse.json()

groqkey = next((item['key'] for item in loginfo if item['api'] == 'GROQ_API_KEY'), None)

def toggle_all(prompt):
    # ここでタブの可視性を切り替えるロジックを追加
    if not prompt == '':
        client = Groq(api_key=groqkey)
    
        response = client.chat.completions.create(
            messages=[
                {
                    "role": "system",
                    "content": "you are a helpful assistant."
                },
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            model="llama-3.3-70b-versatile",
        )
        answer = response.choices[0].message.content
        #answer = prompt+" answered."
        return gr.update(visible=True), answer
    else:
        print("no prompt")

def hello(profile: gr.OAuthProfile | None) -> str:
    # ^ expect a gr.OAuthProfile object as input to get the user's profile
    # if the user is not logged in, profile will be None
    if profile is None:
        return "⛔️"
    return f"ようこそ！ {profile.name}さん"


#def list_private_models(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None) -> str:
def list_private_models(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None):
    # ^ expect a gr.OAuthToken object as input to get the user's token
    # if the user is not logged in, oauth_token will be None
    gr.Textbox(oauth_token)
    if oauth_token is None:
        return "Please log in to list private models.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
    #models = [
        #f"{model.id} ({'private' if model.private else 'public'})"
        #for model in list_models(author=profile.username, token=oauth_token.token)
    #]
    #return "Models:\n\n" + "\n - ".join(models) + ".", gr.update(visible=True)
    return profile.username, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)


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:
        url = 'http://www.ryhintl.com/eprag-be/llm?query='+prompt
        res = requests.get(url)
        rtn = res.content.decode('utf-8')
        return rtn


def process_agent(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 = 'https://www.ryhintl.com/crewai/autogen?qry='+prompt
        res = requests.get(url)
        rtn = res.content.decode('utf-8')
        
        parsed_data = json.loads(rtn)
        content_list = [entry['content'] for entry in parsed_data['chat_history']]

        for content in content_list:
            mycontent = content
           
        return mycontent


with gr.Blocks() as agentic:
    gr.Markdown(
        "# Gradio OAuth Space for Agentic RAG"
        #"\n\nThis Space is a agentic demo for the **Sign in with Hugging Face** feature. "
        #"Duplicate this Space to get started."
        #"\n\nFor more details, check out:"
        #"\n- https://www.gradio.app/guides/sharing-your-app#o-auth-login-via-hugging-face"
        #"\n- https://huggingface.co/docs/hub/spaces-oauth"
    )
    gr.LoginButton()
    # ^ add a login button to the Space
    m1 = gr.Markdown()
    m2 = gr.Markdown()
    agentic.load(hello, inputs=None, outputs=m1)

    with gr.Tab("LLM", visible=False) as tab_llm:
        with gr.Column():
            prompt = gr.Textbox(visible=True, label="プロンプト")
            resp = gr.Textbox(visible=True, label="レスポンス")
            show_button = gr.Button("生成")
            
    show_button.click(fn=toggle_all, inputs=prompt, outputs=[tab_llm, resp])

    with gr.Tab("EPRAG", visible=False) as tab_eprag:
        
        
        gr.Markdown("# 🗞️ AGENTIC EPRAG")
        
        with gr.Row():
            eprag_input = gr.Textbox(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("EPRAGプロセス", variant="primary")
        submit_button.click(
            process_eprag,
            inputs=[eprag_input],
            outputs=[eprag_output]
        )

    with gr.Tab("AGENT", visible=False) as tab_agentic:
        
        
        gr.Markdown("# 🗞️ AGENTIC AUTOGEN")
        
        with gr.Row():
            agent_input = gr.Textbox(label="プロンプト", type="text")
            
        with gr.Row():
            agent_output = gr.Textbox(label="Agentアシスタントの応答")
            
        submit_button = gr.Button("AGENTICプロセス", variant="primary")
        submit_button.click(
            process_agent,
            inputs=[agent_input],
            outputs=[agent_output]
        )
                
    agentic.load(list_private_models, inputs=None, outputs=[m2, tab_llm, tab_eprag, tab_agentic])

agentic.launch()