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| from flask import Flask, request, jsonify | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| app = Flask(__name__) | |
| model_id = "google/gemma-3-1b-it" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| device_map="auto" | |
| ) | |
| def generate(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| out = model.generate( | |
| **inputs, | |
| max_new_tokens=60, | |
| temperature=0.9, | |
| do_sample=True | |
| ) | |
| return tokenizer.decode(out[0], skip_special_tokens=True) | |
| def narrate(): | |
| data = request.json | |
| prompt = f""" | |
| You are Myco, a mystical forest spirit narrator in a browser game. | |
| Respond in 1 short poetic sentence (max 20 words). | |
| Event: {data['event']} | |
| Score: {data.get('score', 0)} | |
| """ | |
| text = generate(prompt) | |
| return jsonify({"text": text}) | |
| app.run(port=5000) | |