app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer
# Load the Hugging Face model
model_path = "patrixtano/mt5-base-anaphora_czech_6e"
model_pipeline = pipeline("text2text-generation", model=model_path)
tokenizer = AutoTokenizer.from_pretrained("patrixtano/mt5-base-anaphora_czech_6e")
def predict(text_input):
    """
    Generate a prediction for the given input text using the Hugging Face model.
    """
    input_length = len(tokenizer(text_input)["input_ids"])
    generation_parameters = {
    "min_length": input_length + 5,  # Set your desired minimum length
    "max_length": input_length + 10  # Set your desired maximum length
    }
    try:
        result = model_pipeline(text_input, **generation_parameters)
        # Extract and return the generated text
        return result[0]["generated_text"]
    except Exception as e:
        return f"Error: {str(e)}"

examples = ["""Miluji ženu s vařečkou, <ana>která</ana> umí vařit.""",
            """Zřejmě to musel fotit nějaký chatař odsousedství, nebo by to mohl taky fotit můj manžel, ale 
<ana>on</ana> se obyčejně k aparátu moc neměl.""",
            """Tomáš se domluvil s Jardou, že <ana>ho</ana> odveze na nádraží."""]
# Define the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=5, label="Input Text"),
    outputs=gr.Textbox(label="Model Output"),
    title="Anaphora resolution demo",
    description="""Enter text into the \"Input Text\" box, include <ana> </ana> tags around the anaphora 
which is to be resolved. The model generates a copy of the text with <ant> </ant> tags marking the 
predicted antecedent. This demo uses the model based on the mT5 base size model.""",
    theme="default",
    examples=examples
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch(share=True)