app.py

import transformers
import torch

model_id = "yodayo-ai/nephra_v1.0"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
    offload_folder="offload",  # Only provided here during model initialization
)

# Define characters and traits
characters = [
    {"name": "Alex", 
     "description": "Alex is a young and ambitious adventurer, full of energy and a thirst for new discoveries. Always ready to face any challenge, he is driven by a desire to explore uncharted places.", 
     "traits": "brave, energetic, optimistic, determined"},
    
    {"name": "Maya", 
     "description": "Maya is a wise and experienced sorceress, with deep knowledge in magic and ancient rituals. She is known for her calm demeanor, analytical mind, and ability to find solutions in difficult situations.",
     "traits": "calm, thoughtful, intuitive, attentive"},
    
    {"name": "Victor", 
     "description": "Victor is a former warrior who gave up fighting for inner peace and harmony. His life experience and pursuit of justice make him a reliable friend and mentor.", 
     "traits": "serious, thoughtful, fair, balanced"}
]

def generate_response(character_name, user_input, max_length=100, temperature=0.7, top_p=0.85, repetition_penalty=1.1):
    # Find the character data
    character = next((c for c in characters if c["name"] == character_name), None)
    if not character:
        return "Character not found."

    # Create the prompt text
    prompt_text = (f"You are {character_name}, {character['description']}. Traits: {character['traits']}. "
                   f"In response to the question '{user_input}', respond {random.choice(['inspired', 'with doubt', 'joyfully', 'thoughtfully', 'skeptically'])}. Please complete the response.")

    # Generate response
    outputs = pipeline(
        prompt_text,
        max_new_tokens=max_length,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repetition_penalty
    )
    
    return outputs[0]['generated_text']

# Gradio Interface
import gradio as gr

iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Dropdown([c["name"] for c in characters], label="Choose Character"),
        gr.Textbox(lines=2, placeholder="Enter your text here..."),
        gr.Slider(20, 200, step=1, value=100, label="Max Length"),
        gr.Slider(0.1, 1.0, step=0.1, value=0.7, label="Temperature"),
        gr.Slider(0.1, 1.0, step=0.05, value=0.85, label="Top-p"),
        gr.Slider(1.0, 2.0, step=0.1, value=1.1, label="Repetition Penalty")
    ],
    outputs="text",
    title="Roleplaying Model Demo",
    description="Generate responses based on the chosen character's traits."
)

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