app2.py

from transformers import pipeline
import gradio as gr

generator = pipeline(
    "text2text-generation",
    model="google/flan-t5-small"
)

def career_advice(age, academic_level, interests, needs_income):
    prompt = f"""
You are an expert career advisor. A student has the following data:
- Age: {age}
- Academic level: {academic_level}
- Interests: {interests}
- Needs income: {needs_income}
Give a clear, realistic, and motivating recommendation about whether the student should:
- Continue studying
- Do vocational training (FP)
- Start working
Explain the reasoning behind your recommendation.
"""
    result = generator(prompt, max_length=200)
    return result[0]['generated_text']

with gr.Blocks(title="Asesor de Carrera Profesional") as demo:
    gr.Markdown(
        """
        #Asesor de Carrera Profesional con IA
        Bienvenido al asesor de carrera basado en el modelo **FLAN-T5**. 
        Introduce tus datos para recibir una recomendación personalizada sobre si deberías **continuar estudiando**, 
        hacer **Formación Profesional (FP)** o **comenzar a trabajar**.
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("## 📝 Datos del Estudiante")
            age_input = gr.Number(label="Edad", minimum=15, maximum=60, step=1)
            academic_input = gr.Textbox(label="Nivel Académico (Ej: ESO, Bachillerato, Grado Universitario, etc.)", placeholder="Ej: Bachillerato de Ciencias")
            interests_input = gr.Textbox(label="Intereses (Palabras clave)", placeholder="Ej: Tecnología, Diseño Gráfico, Cocina, Viajes")
            income_input = gr.Dropdown(choices=["Sí", "No"], label="¿Necesitas generar ingresos inmediatamente?")
            
            run_button = gr.Button("Obtener Recomendación ✨", variant="primary")

        with gr.Column(scale=2):
            gr.Markdown("## 💡 Recomendación Personalizada")
            output_textbox = gr.Textbox(label="Recomendación del Experto", lines=10, interactive=False)

    run_button.click(
        fn=career_advice,
        inputs=[age_input, academic_input, interests_input, income_input],
        outputs=output_textbox
    )
    
    gr.Examples(
        examples=[
            [18, "High School Diploma", "Web design, programming", "No"],
            [25, "University Degree (Incomplete)", "Project management, sales", "Yes"],
            [16, "Compulsory Secondary Education (ESO)", "Mechanics, vehicle repair", "No"]
        ],
        inputs=[age_input, academic_input, interests_input, income_input]
    )
demo.launch()