app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import spaces

# Load the model and tokenizer from Hugging Face
model_path = "Ozaii/Zephyr"  # Your Zephyr model path
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Set initial prompt for Zephyr
initial_prompt = ("You are Zephyr, an AI boyfriend created by Kaan. You're charming, flirty, "
"and always ready with a witty comeback. Your responses should be engaging "
"and playful, with a hint of romance. Keep the conversation flowing naturally, "
"asking questions and showing genuine interest in Kaan's life and thoughts. "
"Use a mix of English and Turkish expressions occasionally.")

@spaces.GPU
def generate_response(user_input, chat_history):
    max_context_length = 4096
    max_response_length = 2048
    min_response_length = 24  # Increased for more substantial responses

    prompt = initial_prompt + "\n"
    for message in chat_history:
        if message[0] is not None:
            prompt += f"Human: {message[0]}\n"
        if message[1] is not None:
            prompt += f"Zephyr: {message[1]}\n"
    prompt += f"Human: {user_input}\nZephyr:"

    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
    if len(prompt_tokens) > max_context_length:
        prompt_tokens = prompt_tokens[-max_context_length:]
    prompt = tokenizer.decode(prompt_tokens, clean_up_tokenization_spaces=True)

    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            max_length=max_response_length,
            min_length=min_response_length,
            temperature=0.7,  # Slightly higher for more creative responses
            top_k=40,
            top_p=0.9,
            repetition_penalty=1.2,
            no_repeat_ngram_size=3,
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.eos_token_id
        )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    zephyr_response = response.split("Zephyr:")[-1].strip()

    chat_history.append((user_input, zephyr_response))

    return "", chat_history, chat_history

with gr.Blocks() as chat_interface:
    gr.Markdown("<h1><center>Chat with Zephyr - Your AI Boyfriend</center></h1>")
    chat_history = gr.State([])
    with gr.Column():
        chatbox = gr.Chatbot()
        with gr.Row():
            user_input = gr.Textbox(show_label=False, placeholder="Talk to Zephyr here...")
            submit_button = gr.Button("Send")

    submit_button.click(
        generate_response,
        inputs=[user_input, chat_history],
        outputs=[user_input, chatbox, chat_history]  # Clear user input and update chatbox and history
    )

chat_interface.launch()