class.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier
import colorama
from colorama import Fore
import csv

colorama.init(autoreset=True)

# =========================
# Carregar dataset
# =========================
df = pd.read_csv("dataset.csv")
texts = df["text"]
labels = df[["joy", "sadness", "anger", "neutral"]]

# =========================
# Treino
# =========================
X_train, X_test, y_train, y_test = train_test_split(
    texts, labels, test_size=0.2, random_state=42
)

vectorizer = TfidfVectorizer(lowercase=True, max_features=5000)
X_train_vec = vectorizer.fit_transform(X_train)

# Criar um SGDClassifier para cada emoção
emotions = ["joy", "sadness", "anger", "neutral"]
models = {}
for emotion in emotions:
    clf = SGDClassifier(loss="log_loss", max_iter=1000)  # removido class_weight
    clf.partial_fit(X_train_vec, y_train[emotion], classes=[0,1])
    models[emotion] = clf

print("Modelo treinado com sucesso.\n")

# =========================
# Funções auxiliares
# =========================
def predict_emotions(text):
    vec = vectorizer.transform([text])
    result = {}
    for emotion, clf in models.items():
        prob = clf.predict_proba(vec)[0][1]
        result[emotion] = prob
    return result

def show_bar(prob, length=20):
    filled_len = int(round(length * prob))
    return '█' * filled_len + '░' * (length - filled_len)

def intensity_label(prob):
    if prob < 0.2:
        return "Muito fraca"
    elif prob < 0.4:
        return "Fraca"
    elif prob < 0.6:
        return "Média"
    elif prob < 0.8:
        return "Forte"
    else:
        return "Muito forte"

def save_to_csv(text, label):
    """Salva a nova frase no dataset.csv"""
    row = [text] + [1 if e == label else 0 for e in emotions]
    with open("dataset.csv", "a", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(row)

# =========================
# Loop interativo
# =========================
print("Classificador de Emoções (digite 'sair' para encerrar)")
print("-" * 50)

while True:
    choice = input("Digite 'classificar', 'treinar' ou 'sair': ").strip().lower()

    if choice in ["sair", "quit"]:
        print("Encerrando classificador.")
        break

    elif choice == "classificar":
        user_input = input("Digite um texto: ").strip()
        if not user_input:
            continue

        result = predict_emotions(user_input)
        dominant = max(result, key=result.get)

        print("\nResultado:")
        for emotion, prob in result.items():
            bar = show_bar(prob)
            label = intensity_label(prob)
            color = {
                "joy": Fore.GREEN,
                "sadness": Fore.BLUE,
                "anger": Fore.RED,
                "neutral": Fore.LIGHTBLACK_EX
            }.get(emotion, Fore.WHITE)
            print(f"{color}{emotion:8}: {bar} {prob:.2f} ({label})")

        print(f"\nEmoção dominante: {dominant} ({intensity_label(result[dominant])})")
        print("-" * 50)

    elif choice == "treinar":
        new_text = input("Digite a nova frase: ").strip()
        new_label = input("Digite a emoção dominante (joy/sadness/anger/neutral): ").strip().lower()

        if new_label in emotions:
            # Treino incremental
            X_new = vectorizer.transform([new_text])
            for emotion in emotions:
                y_new = [1] if emotion == new_label else [0]
                models[emotion].partial_fit(X_new, y_new)
            # Salvar no CSV
            save_to_csv(new_text, new_label)
            print("Nova frase adicionada, modelo atualizado e dataset salvo!")
        else:
            print("Rótulo inválido. Use: joy, sadness, anger ou neutral.")

    else:
        print("Opção inválida. Use 'classificar', 'treinar' ou 'sair'.")