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VoiceAI

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Update app.py

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import gradio as gr
import google.generativeai as genai
import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Configure Gemini API
API_KEY = os.getenv("GOOGLE_API_KEY")  # Ensure this is set in your Hugging Face Spaces secrets
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel("gemini-2.0-flash")
chat = model.start_chat()

# Send initial instruction to the model
initial_message = (
    "Hey, I'm using you as a therapist to help people who are feeling sad feel better. "
    "I want you to improve the user's mental health and make them feel good about themselves, "
    "sort of like a supportive friend. Keep your responses short, concise, and make them feel like they matter. "
    "Do not directly guide them to a suicide prevention hotline, as it will be mentioned on my website already. "
    "Just be there to listen. Start the conversation by saying 'Hi, how are you doing today?'"
)
chat.send_message(initial_message)

# Chat function for Gradio
def chat_function(message, history):
    # Initialize history as a list of messages if empty
    if not history:
        history = []

    # Rebuild Gemini conversation history
    # Clear previous history to avoid duplication (Gemini maintains its own history)
    chat.history = []  # Reset Gemini chat history
    chat.send_message(initial_message)  # Resend initial instruction

    # Send all previous messages from Gradio history to Gemini
    for msg in history:
        role = msg["role"]
        content = msg["content"]
        chat.send_message(content)  # Gemini doesn't need role, just content

    # Send the current user message
    response = chat.send_message(message)

    # Return the user message and assistant response in the messages format
    return [
        {"role": "user", "content": message},
        {"role": "assistant", "content": response.text}
    ]

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Voice: Your Supportive Chatbot")
    gr.Markdown("I'm here to listen and help you feel better. You matter!")
    chatbot = gr.ChatInterface(
        fn=chat_function,
        chatbot=gr.Chatbot(height=500, show_copy_button=True, type="messages"),
        title="Chat with VoiceAI",
        description="A supportive chatbot to lift your spirits.",
        submit_btn="Send"
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()

	import gradio as gr
	import google.generativeai as genai
	import os
	from dotenv import load_dotenv

	# Load environment variables from .env file
	load_dotenv()

	# Configure Gemini API
	API_KEY = os.getenv("GOOGLE_API_KEY") # Ensure this is set in your Hugging Face Spaces secrets
	genai.configure(api_key=API_KEY)
	model = genai.GenerativeModel("gemini-2.0-flash")
	chat = model.start_chat()

	# Send initial instruction to the model
	initial_message = (
	"Hey, I'm using you as a therapist to help people who are feeling sad feel better. "
	"I want you to improve the user's mental health and make them feel good about themselves, "
	"sort of like a supportive friend. Keep your responses short, concise, and make them feel like they matter. "
	"Do not directly guide them to a suicide prevention hotline, as it will be mentioned on my website already. "
	"Just be there to listen. Start the conversation by saying 'Hi, how are you doing today?'"
	)
	chat.send_message(initial_message)

	# Chat function for Gradio
	def chat_function(message, history):
	# Initialize history as a list of messages if empty
	if not history:
	history = []

	# Rebuild Gemini conversation history
	# Clear previous history to avoid duplication (Gemini maintains its own history)
	chat.history = [] # Reset Gemini chat history
	chat.send_message(initial_message) # Resend initial instruction

	# Send all previous messages from Gradio history to Gemini
	for msg in history:
	role = msg["role"]
	content = msg["content"]
	chat.send_message(content) # Gemini doesn't need role, just content

	# Send the current user message
	response = chat.send_message(message)

	# Return the user message and assistant response in the messages format
	return [
	{"role": "user", "content": message},
	{"role": "assistant", "content": response.text}
	]

	# Create Gradio interface
	with gr.Blocks() as demo:
	gr.Markdown("# Voice: Your Supportive Chatbot")
	gr.Markdown("I'm here to listen and help you feel better. You matter!")
	chatbot = gr.ChatInterface(
	fn=chat_function,
	chatbot=gr.Chatbot(height=500, show_copy_button=True, type="messages"),
	title="Chat with VoiceAI",
	description="A supportive chatbot to lift your spirits.",
	submit_btn="Send"
	)

	# Launch the app
	if __name__ == "__main__":
	demo.launch()