app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_name = "Smilyai-labs/Sam-large-v1-speacil"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Chat function
def respond(message, history):
    history = history or []
    chat_prompt = ""
    for user, bot in history:
        chat_prompt += f"User: {user}\nSam: {bot}\n"
    chat_prompt += f"User: {message}\nSam:"

    # Tokenize input and generate a response
    inputs = tokenizer(chat_prompt, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1)

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    reply = response[len(chat_prompt):].split("\n")[0].strip()

    history.append((message, reply))
    return history, history

# Gradio interface
chatbot = gr.Chatbot()
demo = gr.Interface(
    fn=respond,
    inputs=[gr.Textbox(label="Your message"), gr.State()],
    outputs=[chatbot, gr.State()],
    title="🧠 Sam Chatbot",
    description="A demo of Sam-large-v1-speacil: chaotic, aligned, and fun."
)

demo.launch()