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():
    display = ""
    for category, chats in saved_chats.items():
        display += f"<div class='chat-category'><h3>{category}</h3>"
        for user_message, bot_response in chats:
            display += f"<p><strong>You:</strong> {user_message}<br><strong>Bot:</strong> {bot_response}</p>"
        display += "</div>"
    return display.strip()

# 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);
}

.chatbot-container {
    display: flex;
    flex-direction: column;
    justify-content: space-between;
    border-radius: 15px;
    background: rgba(255, 255, 255, 0.8);
    padding: 1rem;
    height: 400px;
    overflow-y: auto;
    box-shadow: 0px 4px 15px rgba(0, 0, 0, 0.2);
}

input, button {
    border: none;
    padding: 0.8rem;
    border-radius: 10px;
    margin: 0.5rem 0;
    outline: none;
}

input {
    background: #fff;
    color: #333;
    font-size: 1rem;
}

button {
    background: linear-gradient(90deg, #6a11cb, #2575fc);
    color: white;
    font-weight: bold;
    cursor: pointer;
    transition: transform 0.2s, background 0.2s;
}

button:hover {
    transform: scale(1.05);
    background: linear-gradient(90deg, #2575fc, #6a11cb);
}

.chat-category {
    background: rgba(0, 0, 0, 0.05);
    border: 2px solid #ff7eb3;
    margin: 1rem 0;
    padding: 1rem;
    border-radius: 10px;
    transition: transform 0.2s, box-shadow 0.2s;
}

.chat-category:hover {
    transform: translateY(-3px);
    box-shadow: 0px 4px 15px rgba(255, 127, 179, 0.5);
}
""")

with chat_interface:
    with gr.Row():
        gr.Markdown("<h1 style='text-align:center;'>🌈 Vibrant Motivational Chatbot</h1>")
    with gr.Row():
        gr.Markdown("*Feeling stressed or unmotivated? Share your thoughts and let me help!*")
    with gr.Row():
        chatbot_output = gr.Chatbot(label="Motivator Bot")
    with gr.Row():
        user_input = gr.Textbox(label="Your Message", placeholder="Type something...")
        send_button = gr.Button("Send")
    with gr.Row():
        saved_chats_display = gr.HTML(label="Saved Chats", value=display_saved_chats())
        refresh_button = gr.Button("Refresh Saved Chats")
    
    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(handle_interaction, inputs=[user_input, chatbot_output], outputs=[chatbot_output, saved_chats_display])
    refresh_button.click(display_saved_chats, inputs=[], outputs=saved_chats_display)

if _name_ == "_main_":
    chat_interface.launch()