import gradio as gr
from transformers import MarianMTModel, MarianTokenizer, GPT2LMHeadModel, GPT2Tokenizer

def translate(text, target_language):
    # ... (keep the existing code for translation here)

def generate_text(prompt):
    model_name = 'gpt2'
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_name)
    inputs = tokenizer.encode(prompt, return_tensors='pt')
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

language_options = [
    # ... (keep the existing language options here)
]

iface_translation = gr.Interface(
    fn=translate,
    # ... (keep the existing translation inputs and outputs here)
)

iface_generation = gr.Interface(
    fn=generate_text,
    inputs=gr.inputs.Textbox(lines=5, label="Enter a prompt for text generation:"),
    outputs=gr.outputs.Textbox(label="Generated Text"),
)

# Combine the two interfaces into a single Gradio interface
iface_combined = gr.Interface(
    [translate, generate_text],
    inputs=[
        gr.inputs.Textbox(lines=5, label="Enter text to translate / generate:", default="Enter text to translate here."),
        gr.inputs.Dropdown(choices=language_options, label="Target Language"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Translated Text / Generated Text"),
    ],
    title="Translation and Text Generation",
    description="Choose a target language to translate English text or leave it as 'None' for text generation.",
    examples=[["Translate this text to French.", "French (European)"]]
)

iface_combined.launch()