| import spaces |
| import json |
| import torch |
| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct" |
| SYSTEM_PROMPT = "You are a helpful assistant. Answer in the same language as the user." |
|
|
| print(f"Loading Model: {MODEL_NAME} ...") |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, dtype=torch.bfloat16) |
| model.to("cuda") |
| model.eval() |
| print("Model loaded!") |
|
|
|
|
| def _text(content): |
| if isinstance(content, str): |
| return content |
| if isinstance(content, list): |
| return "".join(p.get("text", "") if isinstance(p, dict) else str(p) for p in content) |
| return str(content) |
|
|
|
|
| def build_messages(message, history): |
| messages = [{"role": "system", "content": SYSTEM_PROMPT}] |
| for turn in history or []: |
| if isinstance(turn, dict): |
| messages.append({"role": turn["role"], "content": _text(turn["content"])}) |
| else: |
| human, assistant = turn |
| messages.append({"role": "user", "content": _text(human)}) |
| if assistant: |
| messages.append({"role": "assistant", "content": _text(assistant)}) |
| messages.append({"role": "user", "content": _text(message)}) |
| return messages |
|
|
|
|
| @spaces.GPU(duration=60) |
| def generate_response(message, history): |
| messages = build_messages(message, history) |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer([text], return_tensors="pt").to("cuda") |
|
|
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=384, |
| temperature=0.7, |
| top_p=0.9, |
| do_sample=True, |
| ) |
| new_tokens = outputs[0][inputs.input_ids.shape[1]:] |
| return tokenizer.decode(new_tokens, skip_special_tokens=True) |
|
|
|
|
| def api_generate(message: str, history_json: str = "[]") -> str: |
| """Endpoint لأي تطبيق خارجي (Lovable). history_json = JSON list of {role, content}""" |
| try: |
| history = json.loads(history_json) |
| except Exception: |
| history = [] |
| return generate_response(message, history) |
|
|
|
|
| with gr.Blocks() as demo: |
| gr.ChatInterface( |
| fn=generate_response, |
| title="Qwen 2.5 1.5B API & Chat", |
| description="تحدث مع الموديل هنا أو اربطه بأي تطبيق عبر الـ API.", |
| ) |
| gr.api(api_generate, api_name="generate") |
|
|
| if __name__ == "__main__": |
| demo.queue(max_size=10).launch(server_name="0.0.0.0", server_port=7860) |
