import gradio as gr
import openai

# Set OpenAI API Key
openai.api_key = "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"
openai.api_base = "https://api.groq.com/openai/v1"

# Dictionary to store categorized chats
saved_chats = {
    "Stress Management": [],
    "Career Advice": [],
    "General": [],
    "Suggestions": []
}

# Function to get response from GROQ API
def get_groq_response(message):
    try:
        response = openai.ChatCompletion.create(
            model="llama-3.1-70b-versatile",
            messages=[
                {"role": "user", "content": message},
                {"role": "system", "content": "You will talk like a Motivational Speaker to help people come out of stress."}
            ]
        )
        return response.choices[0].message["content"]
    except Exception as e:
        return f"Error: {str(e)}"

# Function to classify messages based on the topic
def classify_message(user_message, bot_response):
    if "stress" in user_message.lower():
        saved_chats["Stress Management"].append((user_message, bot_response))
        return "Stress Management"
    elif "career" in user_message.lower():
        saved_chats["Career Advice"].append((user_message, bot_response))
        return "Career Advice"
    elif "suggestions" in user_message.lower():
        saved_chats["Suggestions"].append((user_message, bot_response))
        return "Suggestions"
    else:
        saved_chats["General"].append((user_message, bot_response))
        return "General"

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

# Function to display saved chats
def display_saved_chats():
    def format_chats(category):
        return "\n".join([f"**You**: {u}\n**Bot**: {b}" for u, b in saved_chats[category]]) or "No messages yet."
    
    return (
        format_chats("Stress Management"),
        format_chats("Career Advice"),
        format_chats("General"),
        format_chats("Suggestions")
    )

# Gradio Interface setup
chat_interface = gr.Blocks(css="""
body {
    font-family: 'Poppins', sans-serif;
    background: linear-gradient(45deg, #ff9a9e, #fad0c4, #fbc2eb, #a1c4fd, #c2e9fb);
    background-size: 400% 400%;
    animation: gradientBG 10s ease infinite;
    margin: 0;
    padding: 0;
    color: #333;
}

@keyframes gradientBG {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}

header, footer {
    text-align: center;
    background: linear-gradient(90deg, #ff758c, #ff7eb3);
    color: white;
    padding: 1rem;
    border-radius: 15px;
    margin-bottom: 1rem;
    box-shadow: 0px 4px 15px rgba(0, 0, 0, 0.2);
}
""")

with chat_interface:
    with gr.Row():
        gr.Markdown("<h1 style='text-align:center;'>🌈 Vibrant Motivational Chatbot</h1>")
    with gr.Row():
        user_input = gr.Textbox(label="Your Message", placeholder="Type something...")
        send_button = gr.Button("Send")
    with gr.Row():
        chatbot_output = gr.Chatbot(label="Chat History")

    with gr.Row():
        with gr.Column():
            stress_display = gr.Textbox(label="Stress Management", interactive=False, lines=10)
        with gr.Column():
            career_display = gr.Textbox(label="Career Advice", interactive=False, lines=10)
        with gr.Column():
            general_display = gr.Textbox(label="General", interactive=False, lines=10)
        with gr.Column():
            suggestions_display = gr.Textbox(label="Suggestions", interactive=False, lines=10)
    
    def handle_interaction(user_input, history):
        if not user_input.strip():
            return history, *display_saved_chats()
        updated_history, _ = chatbot(user_input, history)
        return updated_history, *display_saved_chats()

    send_button.click(
        fn=handle_interaction,
        inputs=[user_input, chatbot_output],
        outputs=[chatbot_output, stress_display, career_display, general_display, suggestions_display]
    )

chat_interface.launch()