## app.py ##
from transformers import pipeline
from gradio import Interface
import gradio as gr

# Create a dictionary of models
MODELS = {
    "T5": "lmsys/fastchat-t5-3b-v1.0",
    "Bert": "bert-base-multilingual-cased",
    "GPT2": "datificate/gpt2-small-spanish",
    "bloom":"bigscience/bloom"
}

# Define your function
def generate_and_analyze(model_name, input_text):
    # Load the model from the dictionary using the selected model name
    model = MODELS[model_name]
    text_generator = pipeline('text-generation', model=model, device=0) # Use GPU if available
    result = text_generator(input_text, max_length=250, do_sample=True)[0]
    return result['generated_text']

# Define your interface
iface = gr.Interface(
    fn=generate_and_analyze, 
    inputs=[
        gr.inputs.Dropdown(choices=list(MODELS.keys()), label="Model"),
        gr.inputs.Textbox(lines=2, label="Input Text")
    ], 
    outputs="text"
)
iface.launch()