app.py

import gradio as gr
import torch
from transformers import PreTrainedTokenizerFast
import os

# -----------------------------
# Load tokenizer
# -----------------------------
tokenizer_path = "./Dave/tokenizer.json"
tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_path)

# -----------------------------
# Load custom model
# -----------------------------
from dave_model import SimpleTransformer  # put your model class in dave_model.py

vocab_size = tokenizer.vocab_size
model_path = "./Dave/pytorch_model.bin"
model = SimpleTransformer(vocab_size=vocab_size)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

# -----------------------------
# Inference function
# -----------------------------
def generate_response(prompt):
    input_ids = torch.tensor([tokenizer.encode(prompt).ids])
    with torch.no_grad():
        output = model(input_ids)
    predicted_ids = output.argmax(-1)[0].tolist()
    response = tokenizer.decode(predicted_ids)
    return response

# -----------------------------
# Gradio interface
# -----------------------------
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=2, placeholder="Type your prompt here..."),
    outputs="text",
    title="Dave – Fully Custom AI",
    description="Interact with your fully custom AI model."
)

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