app.py

import gradio as gr
import openai
import os

# Set OpenAI API Key
openai.api_key = os.getenv("TRY_NEW_THINGS")
openai.api_base = "https://api.groq.com/openai/v1"

# Function to get response from GROQ API
def get_groq_response(message, category):
    # Define system message based on category
    system_messages = {
        "Study Tips": "Provide effective study strategies, time management advice, and tips for staying organized and focused.",
        "Exam Preparation": "Offer tips for preparing for exams, managing anxiety, and optimizing performance on test day.",
        "Project Guidance": "Provide advice on how to tackle academic projects, group assignments, and presentations effectively.",
        "Stress Management": "Offer calming techniques and advice to handle stress effectively.",
        # (Continue with all other categories...)
        "Friendly Buddy": "Respond as a supportive and fun friend. Be informal, cheerful, and light-hearted."
    }
    system_message = system_messages.get(category, "Respond appropriately to the user's input.")
    
    try:
        response = openai.ChatCompletion.create(
            model="llama-3.1-70b-versatile",
            messages=[
                {"role": "system", "content": system_message},
                {"role": "user", "content": message}
            ]
        )
        return response.choices[0].message["content"]
    except Exception as e:
        return f"Error: {str(e)}"

# Chatbot function
def chatbot(user_input, category, history=[]):
    bot_response = get_groq_response(user_input, category)
    history.append((f"You: {user_input}", f"Bot: {bot_response}"))
    return history, history

# Gradio Interface with grouped categories
categories = {
    "Academic Support": [
        "Study Tips", "Exam Preparation", "Project Guidance", "Time Management", "Building Confidence"
    ],
    "Mental & Physical Wellness": [
        "Stress Management", "Motivation & Focus", "Mental Health", "Physical Health", "Work-Life Balance"
    ],
    "Career & Financial": [
        "Networking & Career Building", "Finding Part-Time Jobs", "Scholarships & Financial Aid",
        "Resume Building", "Interview Preparation", "Budgeting Tips", "Skill Development"
    ],
    "Social & Personal Growth": [
        "Friendship & Social Skills", "Exploring New Hobbies", "Clubs & Extracurriculars", "Creative Projects"
    ],
    "Fun & Miscellaneous": [
        "Funny", "Flirty", "Scary", "Business Mind", "Extrovert", "Friendly Buddy"
    ]
}

def get_subcategories(main_category):
    return categories.get(main_category, [])

with gr.Blocks(css="""
/* Add custom CSS here */
""") as chat_interface:
    with gr.Row():
        gr.Markdown("<h1 style='text-align:center;'>🌟 Vibrant Personal Assistant Chatbot 🌈</h1>")
    
    with gr.Row():
        gr.Markdown("<p style='text-align:center;'>Select a category and type your message to get tailored responses.</p>")
    
    with gr.Row():
        main_category = gr.Radio(
            label="Main Category",
            choices=list(categories.keys()),
            value="Academic Support"
        )
        sub_category = gr.Dropdown(
            label="Subcategory",
            choices=get_subcategories("Academic Support")
        )

    def update_subcategories(main_category):
        return gr.Dropdown.update(choices=get_subcategories(main_category), value=None)

    main_category.change(update_subcategories, inputs=main_category, outputs=sub_category)

    with gr.Row():
        user_input = gr.Textbox(label="Your Message", placeholder="Type something...", lines=2)
        send_button = gr.Button("Send")

    with gr.Row():
        chatbot_output = gr.Chatbot(label="Chat History")

    # Add functionality to handle interactions
    def handle_chat(user_input, category, history):
        if not user_input.strip():
            return history, history
        updated_history, _ = chatbot(user_input, category, history)
        return updated_history, updated_history

    send_button.click(
        handle_chat,
        inputs=[user_input, sub_category, chatbot_output],
        outputs=[chatbot_output, chatbot_output]
    )

chat_interface.launch()