app.py

import gradio as gr
import javalang
from typing import Dict, List, Tuple

class JavaPOOEvaluator:
    """Java-Judge: Evaluate POO in Java Code"""
    def __init__(self):
        self.rubric = {
            "classes": 20,
            "objetos": 20,
            "metodos": 20,
            "atributos": 20,
            "encapsulamento": 10,
            "heranca": 10,
            "polimorfismo": 10,
            "abstracao": 10,
        }

    def analyze_classes_and_objects(self, code: str) -> Tuple[float, List[str]]:
        """Avalia a definição e o uso de classes e objetos"""
        score = 0
        feedback = []

        try:
            tree = javalang.parse.parse(code)
            classes = list(tree.filter(javalang.tree.ClassDeclaration))
            
            if classes:
                score += 15
                feedback.append(f"✓ {len(classes)} classe(s) declarada(s) corretamente.")
            else:
                feedback.append("⚠ Nenhuma classe declarada encontrada.")

            objects = [node for _, node in tree.filter(javalang.tree.VariableDeclarator) if 'new' in str(node.initializer)]
            if objects:
                score += 5
                feedback.append(f"✓ {len(objects)} objeto(s) criado(s) corretamente.")
            else:
                feedback.append("⚠ Nenhuma instância de objeto criada.")
        except Exception as e:
            feedback.append("⚠ Erro ao analisar classes e objetos.")

        return score, feedback

    # Métodos de análise para outros critérios continuam iguais ao seu código anterior...

    def evaluate_code(self, code: str) -> Dict:
        """Avalia o código Java"""
        # Avaliações essenciais e bonificadas
        cls_score, cls_feedback = self.analyze_classes_and_objects(code)
        meth_score, meth_feedback = self.analyze_methods(code)

        enc_score, enc_feedback = self.analyze_encapsulation(code)
        inh_score, inh_feedback = self.analyze_inheritance(code)
        poly_score, poly_feedback = self.analyze_polymorphism(code)
        abs_score, abs_feedback = self.analyze_abstraction(code)

        # Pontuações
        essential_score = cls_score + meth_score
        bonus_score = enc_score + inh_score + poly_score + abs_score
        total_score = essential_score + bonus_score
        total_score = min(100, total_score)

        proficiency = "Necessita Melhorias"
        if total_score >= 90:
            proficiency = "Excelente"
        elif total_score >= 75:
            proficiency = "Bom"
        elif total_score >= 60:
            proficiency = "Satisfatório"

        return {
            "total_score": total_score,
            "essential_score": essential_score,
            "bonus_score": bonus_score,
            "proficiency": proficiency,
            "feedback": {
                "classes_objetos": cls_feedback,
                "metodos": meth_feedback,
                "encapsulamento": enc_feedback,
                "heranca": inh_feedback,
                "polimorfismo": poly_feedback,
                "abstracao": abs_feedback
            }
        }

# Interface Gradio
with gr.Blocks(title="Java-Judge: Evaluate POO in Java Code") as demo:
    gr.Markdown("# Avaliador de POO em Java")
    gr.Markdown("### [Visualizar a Rubrica em PDF](rubric.pdf)")
    gr.Markdown("Ou confira a tabela resumida abaixo.")
    gr.Image("rubric_table.png", label="Tabela Resumida da Rubrica")

    upload = gr.File(label="Carregue arquivos Java para avaliação", file_types=[".java"], file_count="multiple")
    evaluate_button = gr.Button("Avaliar Código")
    output = gr.Textbox(label="Resultado da Avaliação", lines=25)

    def evaluate_code_files(files) -> str:
        """Função para avaliar múltiplos arquivos Java"""
        evaluator = JavaPOOEvaluator()
        results = []

        for file in files:
            with open(file.name, 'r', encoding='utf-8') as f:
                code = f.read()
            evaluation = evaluator.evaluate_code(code)

            result = f"\nArquivo: {file.name}\n"
            result += f"Pontuação Total: {evaluation['total_score']:.1f}/100\n"
            result += f"Nível: {evaluation['proficiency']}\n\n"
            result += "Feedback Detalhado:\n"
            for category, comments in evaluation['feedback'].items():
                result += f"\n{category.replace('_', ' ').title()}:\n"
                for comment in comments:
                    result += f"  {comment}\n"
            results.append(result)

        return "\n".join(results)

    evaluate_button.click(fn=evaluate_code_files, inputs=upload, outputs=output)

if __name__ == "__main__":
    demo.launch(share=True)