app.py

import os
import requests
import gradio as gr
import datetime
#from google.colab import userdata

# Retrieve API key from environment variable
groq_api_key = os.getenv("GROQ_API_KEY")
if not groq_api_key:
   raise ValueError("GROQ_API_KEY environment variable is not set.")

# Function to process user input using Groq's LLM
def process_user_input(user_input, history):
    groq_api_url = "https://api.groq.com/openai/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {groq_api_key}",
        "Content-Type": "application/json"
    }
    messages = [{"role": "system", "content": "You are a compassionate mental health support assistant."}]
    for i in range(0, len(history), 2):
        messages.append({"role": "user", "content": history[i]})
        if i + 1 < len(history):
            messages.append({"role": "assistant", "content": history[i + 1]})
    messages.append({"role": "user", "content": user_input})
    data = {
        "model": "llama-3.3-70b-versatile",
        "messages": messages,
        "temperature": 0.7
    }

    response = requests.post(groq_api_url, headers=headers, json=data)
    if response.status_code == 200:
        return response.json()["choices"][0]["message"]["content"]
    else:
        print(f"Error {response.status_code}: {response.text}")
        return "I'm sorry, I couldn't process your request at the moment."

# Function to log conversation
def log_conversation(user_input, bot_response):
    timestamp = datetime.datetime.now().isoformat()
    with open("conversation_log.txt", "a") as log_file:
        log_file.write(f"{timestamp}\nUser: {user_input}\nBot: {bot_response}\n\n")

# Gradio Interface
with gr.Blocks(css="""
    #chatbox { border-radius: 20px; background-color: #f5f8fa; padding: 15px; }
    .gr-button { border-radius: 10px !important; padding: 10px 20px !important; }
    .gr-textbox textarea { font-size: 16px !important; padding: 10px !important; border-radius: 12px !important; }
    .gr-checkbox label { font-size: 14px; }
""") as demo:
    gr.Markdown("""
        <div style='text-align: center;'>
            <h1>🧠 AI-Powered Mental Health Support Chatbot</h1>
            <p style='color: gray;'>I'm here to listen, support, and provide guidance. All responses are private and confidential.</p>
        </div>
    """)

    with gr.Row():
        emotion = gr.Dropdown(
            choices=[
                "I am feeling happy", "I am feeling sad", "I am feeling angry",
                "I am excited", "I am anxious", "I am tired", "I am overwhelmed", "Typing.."
            ],
            label="Select your current emotion",
            value=None,
            allow_custom_value=False
        )

    with gr.Row():
        msg = gr.Textbox(
            label="You can type here",
            placeholder="💬 Share what's on your mind...",
            lines=2,
            max_lines=4,
            elem_id="user_input"
        )
        send_btn = gr.Button("Send", scale=2)

    chatbot = gr.Chatbot(elem_id="chatbox", height=300)

    consent = gr.Checkbox(label="I consent to have this conversation logged for improvement purposes.", value=False)
    clear = gr.Button("🧹 Clear Chat")

    def respond(emotion, user_message, chat_history, consent):
        full_message = ""
        if emotion:
            full_message += emotion + ". "
        if user_message:
            full_message += user_message
        if not full_message.strip():
            return "", chat_history  # nothing to send
        bot_message = process_user_input(full_message, [item for sublist in chat_history for item in sublist])
        chat_history.append((full_message, bot_message))
        if consent:
            log_conversation(full_message, bot_message)
        return "", chat_history

    send_btn.click(respond, [emotion, msg, chatbot, consent], [msg, chatbot])
    msg.submit(respond, [emotion, msg, chatbot, consent], [msg, chatbot])
    clear.click(lambda: [], None, chatbot, queue=False)

demo.launch()