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from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Modell: Deutsch, öffentlich zugänglich
MODEL_NAME = "dbmdz/german-gpt2"
# Tokenizer & Modell laden
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
app = Flask(__name__)
@app.route("/chat", methods=["POST"])
def chat():
data = request.json
user_input = data.get("message", "")
# Eingabe tokenisieren
input_ids = tokenizer.encode(user_input, return_tensors="pt")
# Modell antworten lassen
output_ids = model.generate(input_ids, max_new_tokens=100, do_sample=True, top_k=50)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
# Antwort ohne ursprüngliche Eingabe zurückgeben
answer_only = response[len(user_input):].strip()
return jsonify({"response": answer_only})
if __name__ == "__main__":
app.run(debug=True) |