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from flask import Flask, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
app = Flask(__name__)
model_path = "./llama"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
device_map="auto"
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
@app.route('/generate', methods=['POST'])
def generate_response():
input_data = request.json
prompt = input_data.get("prompt", "")
if not prompt:
return jsonify({"error": "No prompt provided"}), 400
# Tokenize the prompt and generate response
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return jsonify({"response": response})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
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