Update app.py

app.py

@@ -1,237 +0,0 @@
-import gradio as gr
-from openai import OpenAI
-import os
-import random
-import asyncio
-
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-DEFAULT_LANGUAGE = "English"
-WELCOME_MESSAGE_EN = "Hi! I'm your friendly assistant 🤖 Let's begin!\n\nPlease tell me which language you'd like to use."
-
-# === Normal Question Bank ===
-NORMAL_SET_I = [
-    "When was the last time you walked for more than an hour? Describe where you went and what you saw.",
-    "What is the best gift you have ever received? Why?",
-    "If you had to leave California, where would you move to? What do you miss most about California?",
-    "How did you celebrate Halloween last year?",
-    "Do you read newspapers often? Which newspapers do you like? Why?",
-    "What is the ideal number of students to share a house with? Why?",
-    "If you could invent a new flavor of ice cream, what flavor would you create?",
-    "What is the best restaurant you have been to in the past month that your partner has not been to? Describe it to your partner.",
-    "Describe the pet you last got.",
-    "What is your favorite holiday? Why?",
-    "Tell your partner the funniest thing that happened when you were with a child."
-]
-NORMAL_SET_II = [
-    "What gift did you get on your last birthday?",
-    "Describe your last trip to the zoo.",
-    "Name and age of your family members, including grandparents, uncles and aunts, and where they were born (to the extent known).",
-    "One person says a word, and the next person says a word starting with the last letter of the previous word. Continue for 50 words. No need to form sentences.",
-    "Do you like to wake up early or stay up late? Has anything interesting ever happened to you because of this?",
-    "Where are you from? List all the places you have lived.",
-    "What is your favorite class at UC Santa Cruz? Why?",
-    "What did you do this summer?",
-    "What gifts did you receive last Christmas/Hanukkah?",
-    "Who is your favorite same-sex actor? Describe a great scene he or she starred in.",
-    "What was your impression of UC Santa Cruz when you first came to it?",
-    "What is the best TV show you have seen in the past month that your partner has not seen? Describe it to your partner.",
-    "What is your favorite holiday? Why?"
-]
-NORMAL_SET_III = [
-    "Where did you go to high school? What was it like?",
-    "What is the best book you have read in the past three months that your partner has not read? Describe it to your partner.",
-    "What country would you most like to visit? What attracts you to it?",
-    "Do you prefer digital or analog watches/clocks? Why?",
-    "Describe your mother's best friend.",
-    "What are the pros and cons of artificial Christmas trees?",
-    "How often do you get your hair cut? Where do you go? Have you ever had a bad haircut?",
-    "Did you have a class pet in elementary school? Do you remember its name?",
-    "Do you think left-handed people are more creative than right-handed people?",
-    "What was the last concert you attended? How many albums of the band do you have? Have you seen them perform before? Where?",
-    "What magazines do you subscribe to? What have you subscribed to in the past?",
-    "Have you ever participated in a school play? What role did you play? What was the plot? Did anything funny happen during the play?"
-]
-
-def generate_question_set():
-    return random.sample(NORMAL_SET_I, 3) + random.sample(NORMAL_SET_II, 3) + random.sample(NORMAL_SET_III, 3)
-
-async def gpt_translate(text, target_lang):
-    if target_lang == "English":
-        return text
-    prompt = f"Translate the following into {target_lang}:\n\n{text}"
-    response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}],
-        temperature=0.3
-    )
-    return response.choices[0].message.content.strip()
-
-async def respond(user_input, history, step, language, questions, followup_mode):
-    history.append({"role": "user", "content": user_input})
-    if language == "":
-        user_language = user_input.strip().capitalize()
-        questions_en = generate_question_set()
-        translated_questions = [await gpt_translate(q, user_language) for q in questions_en]
-        welcome = await gpt_translate("Great! We will now continue in your selected language.", user_language)
-        history.append({"role": "assistant", "content": f"{welcome}\n\n1. {translated_questions[0]}"})
-        return history, "", 1, user_language, translated_questions, False
-    if step >= len(questions):
-        thank_you = await gpt_translate("Thank you for your response!", language)
-        end_note = await gpt_translate("This concludes our questions. Please proceed with the rest of the survey. 📝", language)
-        history.append({"role": "assistant", "content": thank_you})
-        history.append({"role": "assistant", "content": end_note})
-        return history, "", step, language, questions, False
-    if followup_mode:
-        comment_prompt = [
-            {"role": "system", "content": f"Write a short, empathetic comment in {language} responding to the user's last answer."},
-            history[-2],
-            history[-1]
-        ]
-        comment_response = client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=comment_prompt,
-            temperature=0.7
-        )
-        comment = comment_response.choices[0].message.content.strip()
-        history.append({"role": "assistant", "content": ""})
-        for c in comment:
-            history[-1]["content"] += c
-            await asyncio.sleep(0.03)
-        next_question = f"{step+1}. {questions[step]}"
-        history.append({"role": "assistant", "content": ""})
-        for c in next_question:
-            history[-1]["content"] += c
-            await asyncio.sleep(0.03)
-        return history, "", step + 1, language, questions, False
-
-    comment_prompt = [
-        {"role": "system", "content": f"You are a friendly and witty assistant. Write a short, personalized comment on the user's answer in {language}. Add a light emoji at the end."},
-        history[-2],
-        history[-1]
-    ]
-    comment_response = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=comment_prompt,
-        temperature=0.8
-    )
-    bot_reply = comment_response.choices[0].message.content.strip()
-
-    followup_count = 0
-    rand = random.random()
-    if rand < 0.1:
-        followup_count = 2
-    elif rand < 0.5:
-        followup_count = 1
-
-    if followup_count > 0:
-        followup_prompt = [
-            {"role": "system", "content": f"Ask {followup_count} open-ended follow-up question(s) based on the user's last answer in {language}."},
-            history[-2],
-            history[-1]
-        ]
-        followup_response = client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=followup_prompt,
-            temperature=0.7
-        )
-        bot_reply += "\n\n" + followup_response.choices[0].message.content.strip()
-        history.append({"role": "assistant", "content": ""})
-        for c in bot_reply:
-            history[-1]["content"] += c
-            await asyncio.sleep(0.03)
-        return history, "", step, language, questions, True
-
-    history.append({"role": "assistant", "content": ""})
-    for c in bot_reply:
-        history[-1]["content"] += c
-        await asyncio.sleep(0.03)
-    next_question = f"{step+1}. {questions[step]}"
-    history.append({"role": "assistant", "content": ""})
-    for c in next_question:
-        history[-1]["content"] += c
-        await asyncio.sleep(0.03)
-    return history, "", step + 1, language, questions, False
-
-def init():
-    return [{"role": "assistant", "content": WELCOME_MESSAGE_EN}], 0, "", [], False
-
-with gr.Blocks(css="""
-.send-btn {
-  background-color: #25D366 !important;
-  color: white !important;
-  border: none;
-  border-radius: 24px;
-  font-size: 20px;
-  padding: 8px 20px;
-  height: 48px;
-  width: 60px;
-  margin-left: 8px;
-  cursor: pointer;
-}
-#chatbox .avatar-container {
-    width: 64px !important;
-    height: 64px !important;
-    min-width: 64px !important;
-    min-height: 64px !important;
-    background-size: 100% 100% !important;
-    background-position: center center !important;
-    border-radius: 50% !important;
-    padding: 0 !important;
-    margin: 0 !important;
-    border: none !important;
-    box-shadow: none !important;
-    background-color: transparent !important;
-}
-#chatbox .message.user { background-color: #DCF8C6 !important; }
-#chatbox .message.assistant { background-color: #ffffff !important; }
-""") as demo:
-    gr.HTML("""
-    <script>
-      const waitForChatbox = () => {
-        const chatbox = document.getElementById("chatbox");
-        if (chatbox) {
-          const observer = new MutationObserver(() => {
-            chatbox.scrollTop = chatbox.scrollHeight;
-          });
-          observer.observe(chatbox, { childList: true, subtree: true });
-        } else {
-          setTimeout(waitForChatbox, 500);
-        }
-      };
-      waitForChatbox();
-    </script>
-    """)
-
-    chatbot = gr.Chatbot(
-        elem_id="chatbox",
-        label="",
-        avatar_images=["user_avatar.jpg", "humanlike_avatar.png"],
-        bubble_full_width=False,
-        height=500,
-        show_copy_button=False,
-        type="messages"
-    )
-
-    with gr.Row():
-        user_input = gr.Textbox(
-            show_label=False,
-            placeholder="Type your message here and press send...",
-            container=True,
-            scale=10
-        )
-        send_btn = gr.Button(value="➤", elem_classes="send-btn")
-
-    state = gr.State([])
-    step = gr.State(0)
-    language = gr.State("")
-    questions = gr.State([])
-    followup_mode = gr.State(False)
-
-    demo.load(fn=init, outputs=[chatbot, step, language, questions, followup_mode])
-    user_input.submit(respond, [user_input, state, step, language, questions, followup_mode],
-                      [chatbot, user_input, step, language, questions, followup_mode])
-    send_btn.click(respond, [user_input, state, step, language, questions, followup_mode],
-                   [chatbot, user_input, step, language, questions, followup_mode])
-
-    demo.queue().launch()