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Create app.py

by myopera9 - opened Mar 3, 2025

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+import gradio as gr
+from groq import Groq
+import io
+import numpy as np
+import soundfile as sf
+import requests
+import cohere
+import json
+import os
+from dotenv import load_dotenv
+# Set up API keys
+load_dotenv(verbose=True)
+coherekey = os.environ.get("COHERE_API_KEY")
+groqkey = os.environ.get("GROQ_API_KEY")
+geturl = os.environ.get("SQLURL")
+co = cohere.ClientV2(api_key=coherekey)
+cresponse = requests.get(geturl)
+cohere_doc = cresponse.json()
+logged_in = None
+def auth(user_name, password):
+    if password == user_name + str(len(user_name)):
+        return True # 認証成功
+    else:
+        return False # 認証失敗
+def login(user_name, password):
+    global logged_in
+    sqlcmd = "https://www.ryhintl.com/dbjson/getjson?sqlcmd=select count(customername) as auth from llm_acl where customername = '"+user_name+"' and customerkey = '"+password+"'"
+    lresponse = requests.get(sqlcmd)
+    #logged = lresponse.content.decode('utf-8')
+    loginfo= lresponse.json()
+    #print(loginfo[0]['auth'])
+    if loginfo[0]['auth'] == "1":
+        logged_in = True
+        return True
+    else:
+        logged_in = False
+        return False
+#def nav_to(url):
+    #nav_script = f"""
+        #<meta http-equiv="refresh" content="0; url='{url}'">
+    #"""
+    #return nav_script
+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 = 'AGENTIC RAG';
+    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';
+}
+"""
+def log_out():
+    return gr.HTML("""
+        <html>
+        <head>
+            <style>
+                body {
+                    font-family: Arial, sans-serif;
+                    background-color: #f0f0f0;
+                    display: flex;
+                    justify-content: center;
+                    align-items: center;
+                    height: 100vh;
+                    margin: 0;
+                }
+                .container {
+                    text-align: center;
+                    background: white;
+                    padding: 50px;
+                    border-radius: 10px;
+                    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+                }
+                h1 {
+                    color: #333;
+                }
+                p {
+                    color: #666;
+                }
+                a {
+                    display: inline-block;
+                    margin-top: 20px;
+                    padding: 10px 20px;
+                    color: white;
+                    /*background-color: #007bff;*/
+                    background-color: green;
+                    text-decoration: none;
+                    border-radius: 5px;
+                }
+                a:hover {
+                    background-color: #0056b3;
+                }
+            </style>
+        </head>
+        <body>
+            <div class="container">
+                <h1>正常にログアウトしました。</h1>
+                <p>ログアウトされました。ご訪問いただきありがとうございます！</p>
+                <a href="/logout" style="background-color:green; color: white;">Go to Login Page</a>
+            </div>
+        </body>
+        </html>
+    """)
+def redirect():
+    global logged_in
+    logged_in = False
+    return gr.HTML("<meta http-equiv='refresh' content='0; url=\"https://www.ryhintl.com/fastclient/logout.html\"'>")
+    #return gr.HTML("<meta http-equiv='refresh' content='0; url=\"/\"'>")
+def load_css():
+    #link = "https://www.ryhintl.com/theme.css"
+    #css_content1 = requests.get(link).content.decode('utf-8')
+    #print(css_content1)
+    with open('theme.css', 'r') as file:
+        css_content = file.read()
+    return css_content
+def transcribe_audio(audio, api_key):
+    if audio is None:
+        return ""
+    # Convert audio to the format expected by the model
+    # The model supports mp3, mp4, mpeg, mpga, m4a, wav, and webm file types
+    audio_data = audio[1]  # Get the numpy array from the tuple
+    buffer = io.BytesIO()
+    sf.write(buffer, audio_data, audio[0], format='mp3')
+    buffer.seek(0)
+    bytes_audio = io.BytesIO()
+    np.save(bytes_audio, audio_data)
+    bytes_audio.seek(0)
+    try:
+        client = Groq(api_key=groqkey)
+        # Use Distil-Whisper English powered by Groq for transcription
+        completion = client.audio.transcriptions.create(
+            #model="distil-whisper-large-v3-en",
+            model="whisper-large-v3-turbo",
+            file=("audio.mp3", buffer),
+            response_format="text"
+        )
+        return completion
+    except Exception as e:
+        return f"エラー: {str(e)}"
+def generate_response(transcription, api_key):
+    if not transcription:
+        return "トランスクリプトが利用できません。もう一度話してみてください。"
+    try:
+        url = 'https://www.ryhintl.com/crewai/autogen?qry='+transcription
+        res = requests.get(url)
+        # Extract content of Professional_Assistant_Agent
+        data = res.content.decode("utf-8")
+        data = data.replace("null","None")
+        datas = eval(data)
+        basic_content = [entry["content"] for entry in datas["chat_history"] if entry["name"] == "Basic_Assistant_Agent"]
+        basic_result = ', '.join([str(x) for x in basic_content])
+        professional_content = [entry["content"] for entry in datas["chat_history"] if entry["name"] == "Professional_Assistant_Agent"]
+        professional_result = ', '.join([str(x) for x in professional_content])
+        #combined_list = basic_content + professional_content
+        final_result = "Basic_Assistant: "+basic_result+"\n\n\nProfessional_Assistant: "+professional_result
+        return final_result
+    except Exception as e:
+        return f"エラー: {str(e)}"
+def process_audio(audio, api_key, prompt):
+    global logged_in
+    if not logged_in:
+        raise gr.Error("ログインセッションが存在しません。ログインし直してください。1")
+    if not prompt == "":
+        transcription = prompt
+        response = generate_response(transcription, api_key)
+        return transcription, response
+    else:
+        transcription = transcribe_audio(audio, api_key)
+        response = generate_response(transcription, api_key)
+        return transcription, response
+def process_cohere(prompt):
+    global logged_in
+    if not logged_in:
+        raise gr.Error("ログインセッションが存在しません。ログインし直してください。2")
+    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:
+        system_message = """## あなたは、LLMのスペシャリストです。"""
+        messages = [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": prompt},
+        ]
+        # Step 2: Tool planning and calling
+        response = co.chat(
+            model="command-r-plus-08-2024",
+            messages=messages,
+            documents=cohere_doc
+        )
+        return response.message.content[0].text
+def process_eprag(prompt):
+    global logged_in
+    if not logged_in:
+        raise gr.Error("ログインセッションが存在しません。ログインし直してください。3")
+    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
+# Custom CSS for the Groq badge and color scheme (feel free to edit however you wish)
+custom_css = """
+.gradio-container {
+    background-color: #f5f5f5;
+}
+.gr-button-primary {
+    background-color: #f55036 !important;
+    border-color: #f55036 !important;
+}
+.gr-button-secondary {
+    color: #f55036 !important;
+    border-color: #f55036 !important;
+}
+#groq-badge {
+    position: fixed;
+    bottom: 20px;
+    right: 20px;
+    z-index: 1000;
+}
+"""
+#with gr.Blocks(theme=gr.themes.Default()) as llm:
+with gr.Blocks(css=load_css(),js=js) as llm:
+    with gr.Tab("VAR"):
+        gr.Markdown("# 🎙️ VOICE AGENTIC RAG")
+        #api_key_input = gr.Textbox(type="password", label="Groq API Keyを入力してください。", value=groqkey, visible=False)
+        api_key_input = groqkey
+        with gr.Row():
+            audio_input = gr.Audio(label="音声プロンプト", type="numpy")
+        with gr.Row():
+            user_input = gr.Textbox(label="プロンプト", type="text")
+        with gr.Row():
+            transcription_output = gr.Textbox(label="トランスクリプション")
+            response_output = gr.Textbox(label="AIアシスタントの応答")
+        submit_button = gr.Button("プロセス", variant="primary")
+        # Add the Groq badge
+        gr.HTML("""
+        <div id="groq-badge">
+            <div style="color: #f55036; font-weight: bold;">POWERED BY EPRAG</div>
+        </div>
+        """)
+        submit_button.click(
+            process_audio,
+            inputs=[audio_input, api_key_input, user_input],
+            outputs=[transcription_output, response_output]
+        )
+        gr.Markdown("""
+        ## 使い方:
+        1. マイクのアイコンをクリックしてメッセージを入力するかプロンプトのプロンプトを入力してください。
+        2. 音声入力する場合、マイクのアイコンをクリックしてメッセージを話してください。 サポートされている音声ファイルを提供することもできます。サポートされているオーディオ・ファイルには、mp3、mp4、mpeg、mpga、m4a、wav、webmなどがあります。
+        3. [プロセス] ボタンをクリックしてスピーチを文字に起こし、AGENTIC RAG アシスタントからの応答を生成します。
+        4. 文字起こしとAIアシスタントの応答がそれぞれのテキスト・ボックスに表示されます。
+        """)
+    with gr.Tab("COHERE"):
+        gr.Markdown("# 📂 COHERE AGENTIC RAG")
+        with gr.Row():
+            cohere_input = gr.Textbox(label="プロンプト", type="text")
+        with gr.Row():
+            cohere_output = gr.Textbox(label="AIアシスタントの応答")
+        submit_button = gr.Button("COHEREプロセス", variant="primary")
+        submit_button.click(
+            process_cohere,
+            inputs=[cohere_input],
+            outputs=[cohere_output]
+        )
+    with gr.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("アカウント"):
+        gr.Markdown("# 🏃🏽‍➡️ ログアウト")
+        with gr.Row():
+            #logout_output = gr.Textbox(label="AIアシスタントの応答")
+            logout_output = gr.HTML(label="AIアシスタントの応答")
+        submit_button = gr.Button("ログアウト", variant="primary")
+        submit_button.click(
+            redirect,
+            inputs=[],
+            outputs=[logout_output]
+        )
+#with llm.route("ログアウト", "/signout"):
+    #log_out_button = gr.Button("ログアウト")
+    #output = gr.HTML()
+    #log_out_button.click(fn=log_out, inputs=None, outputs=output)
+llm.launch(share=True)
+#llm.launch(auth=[("ryhintl", "ryhintl")],share=True,pwa=True)
+#llm.launch(auth=[("ryhintl", "ryhintl")],share=True,server_name="localhost", server_port=7066)