from huggingface_hub import from_pretrained_fastai
import gradio as gr

# Cargar el modelo desde Hugging Face Hub
repo_id = "dagarcsot/entregable3"
learner = from_pretrained_fastai(repo_id)

def predict_sentiment(text):
    pred, pred_idx, probs = learner.predict(text)
    prob_positive = float(probs[1])
    
    # Seleccionar imagen según rango de positividad
    if prob_positive < 0.25:
        image = "very_sad.png"
        nivel = "Very negative"
    elif prob_positive < 0.5:
        image = "sad.png"
        nivel = "Negative"
    elif prob_positive < 0.75:
        image = "happy.png"
        nivel = "Positive"
    else:
        image = "very_happy.png"
        nivel = "Veery positive"

    mensaje = f"{nivel} ({prob_positive*100:.2f}%)"
    return mensaje, image

# Interfaz Gradio
gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=3, placeholder="Escribe algo..."),
    outputs=[
        gr.Text(label="Evaluación"),
        gr.Image(type="filepath", label="Expresión facial")
    ],
    title="Clasificador de Sentimiento",
    description="This model evaluates how positive a text is and represents it with a face. (Trained on movie reviews).",
    examples=[
        "I love this thing.",
        "Good, I think?.",
        "It had some very good moments, but sometimes it was little boring. I love Danny DeVito",
        "I hate it."
    ]
).launch()