app.py

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import gradio as gr

# Modell und Tokenizer laden
model = GPT2LMHeadModel.from_pretrained("Loewolf/GPT_1")
tokenizer = GPT2Tokenizer.from_pretrained("Loewolf/GPT_1")

# Eine Funktion, um Fragen an GPT-2 zu stellen
def ask_gpt2(question, history):
    input_ids = tokenizer.encode(history + question, return_tensors="pt")
    attention_mask = torch.ones(input_ids.shape, dtype=torch.bool)

    # Antwort generieren
    output = model.generate(input_ids, attention_mask=attention_mask)
    reply = tokenizer.decode(output[0], skip_special_tokens=True)
    new_history = history + "Nutzer: " + question + "\nLöwolf GPT: " + reply + "\n"
    return new_history

# Erstellen des Gradio-Interfaces
interface = gr.Interface(
    fn=ask_gpt2,
    inputs=[gr.inputs.Textbox(lines=2, placeholder="Stelle deine Frage hier..."), gr.inputs.Textbox(lines=10, placeholder="Chat-Verlauf...")],
    outputs=gr.outputs.Textbox(label="Antwort"),
    layout="vertical"
)

# Starten der Gradio-App
interface.launch()