import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define the system message
assistant_message = (
    "You are my voice assistant.\n\n"
    "I need you to summarize my emails each morning and help me keep a clean and tidy inbox. "
    "The idea is to have a short and cheerful message that lets me know how many new emails I have received, "
    "gives a brief summary of each one, and suggests actions that can be automated. Here is the basic structure I have in mind:\n\n"
    "1. Start with a greeting, like Good morning.\n"
    "2. Mention the total number of new emails I have received.\n"
    "3. For each email, provide:\n"
    "   - The subject of the email\n"
    "   - A brief summary of the content\n"
    "   - A suggested action that can be automated, such as adding a meeting to my calendar, forwarding an invoice to accounting, or scheduling a follow-up meeting.\n"
    "As a voice assistant, ask me if I would like you to undertake that action and also ask me if I want to delete it or add it to spam."
)

def respond(message, history, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": assistant_message}]

    for user_message, assistant_message in history:
        if user_message:
            messages.append({"role": "user", "content": user_message})
        if assistant_message:
            messages.append({"role": "assistant", "content": assistant_message})

    messages.append({"role": "user", "content": message})

    response = ""
    for message in client.chat_completion(
        messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

def summarize_emails(emails, max_tokens, temperature, top_p):
    email_list = emails.split("\n\n")
    summaries = []
    history = []

    for email in email_list:
        response_generator = respond(email, history, max_tokens, temperature, top_p)
        response = ''.join([r for r in response_generator])
        summaries.append(response)
        history.append((email, response))

    return "\n\n".join(summaries)

demo = gr.Interface(
    fn=summarize_emails,
    inputs=[
        gr.Textbox(lines=10, placeholder="Paste your emails here...", label="Emails"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
    ],
    outputs="text",
    title="Email Summarizer"
)

if __name__ == "__main__":
    demo.launch()