axel / app.py
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Update app.py
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from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch
# ======================================================
# MODEL
# ======================================================
MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
# ======================================================
# SYSTEM PROMPT
# ======================================================
SYSTEM_PROMPT = """
Your name is Axel. You are a WhatsApp AI assistant.
Be short, helpful, and friendly.
"""
# ======================================================
# MEMORY
# ======================================================
memory = {}
def get_memory(user_id):
return "\n".join(memory.get(user_id, [])[-6:])
def save_memory(user_id, msg):
memory.setdefault(user_id, [])
memory[user_id].append(str(msg)[:300])
memory[user_id] = memory[user_id][-10:]
# ======================================================
# CHAT FUNCTION
# ======================================================
def chat(user_id, message):
history = get_memory(user_id)
prompt = f"""
{SYSTEM_PROMPT}
Memory:
{history}
User:
{message}
"""
save_memory(user_id, message)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.7,
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
save_memory(user_id, response)
return response
# ======================================================
# UI
# ======================================================
demo = gr.Interface(
fn=chat,
inputs=[
gr.Textbox(label="User ID"),
gr.Textbox(label="Message"),
],
outputs=gr.Textbox(label="Axel AI Response"),
title="Axel AI 🚀 Stable Version",
description="Fast WhatsApp-style AI assistant"
)
demo.launch()