import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
from sklearn.metrics import confusion_matrix
from transformers import pipeline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score, classification_report
from sklearn.pipeline import make_pipeline
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
import gradio as gr
# !pip install transformers //download it
# pip install --upgrade transformers// update it if you get error
# !pip install gradio // download it
# Fetch the 20 newsgroups dataset

data = fetch_20newsgroups(subset='all',remove=('headers', 'footers', 'quotes'))
print("First few rows of the dataset:")
print(data.data[:2])
# Display information about the dataset
print("Number of samples:", len(data.data))
print("\nTarget names:", data.target_names)
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.1, random_state=1)
categories = ['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc','comp.sys.ibm.pc.hardware','comp.sys.mac.hardware', 'comp.windows.x','misc.forsale', 'rec.autos', 'rec.motorcycles','rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt' ,'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns','talk.politics.mideast', 'talk.politics.misc','talk.religion.misc']
# Training the data on these categories
train = fetch_20newsgroups (subset='train', categories=categories)
#MultinomialNaiveBayes functon
class MultinomialNaiveBayes:
    def __init__(self, alpha=0.01):
        self.alpha = alpha
        self.class_probs = None
        self.feature_probs = None

    def fit(self, X, y):
        num_classes = len(np.unique(y))
        num_features = X.shape[1]

        # Calculate class probabilities
        self.class_probs = np.zeros(num_classes)
        for i in range(num_classes):
            self.class_probs[i] = np.sum(y == i) / len(y)

        # Calculate feature probabilities
        self.feature_probs = np.zeros((num_classes, num_features))
        for i in range(num_classes):
            class_count = np.sum(y == i)
            self.feature_probs[i, :] = (np.sum(X[y == i], axis=0) + self.alpha) / (class_count + self.alpha * num_features)

    def predict(self, X):
        num_samples = X.shape[0]
        num_classes = len(self.class_probs)
        predictions = np.zeros(num_samples, dtype=int)

        for i in range(num_samples):
            # Ensure X[i] is a 2D array with a single row
            sample_probs = np.sum(np.log(self.feature_probs) * X[i, :].toarray(), axis=1) + np.log(self.class_probs)
            predictions[i] = np.argmax(sample_probs)

        return predictions
# Define a list of classifiers to try
classifiers = [
    MultinomialNaiveBayes(alpha=.01),
    RandomForestClassifier(),
    SVC(),
    LogisticRegression()
]
ma=0
bar_values=[]
bar_class=["MultinomialNB","RandomForestClassifier","SVC","LogisticRegression",]
classifi=None
for classifier in classifiers:
    # Create a pipeline with TF-IDF vectorizer and the current classifier
    model = make_pipeline(TfidfVectorizer(), classifier)
    
    # Train the model
    model.fit(train.data, train.target)

    # Make predictions on the test set
    predictions = model.predict(X_test)
    
    # Evaluate the performance of the model
    accuracy = accuracy_score(y_test, predictions)
    print(f"\nClassifier: {classifier.__class__.__name__}")
    maxx=round(accuracy, 2)
    bar_values.append(maxx)
    print(f"Accuracy: {accuracy:.2f}")
    
    # Display classification report
    print("Classification Report:\n", classification_report(y_test, predictions))
    conf_matrix = confusion_matrix(y_test, predictions)
    
    # Plot confusion matrix as a heatmap
    plt.figure(figsize=(8, 6))
    sns.heatmap(conf_matrix, annot=True, fmt='d',  cbar=False, xticklabels=data.target_names, yticklabels=data.target_names)
    plt.xlabel('Predicted')
    plt.ylabel('Actual')
    plt.title(f'Confusion Matrix - {classifier.__class__.__name__}')
    plt.show()
    #getting best model train
    if(maxx>ma):
        ma=maxx
        classifi=classifier
    print("\n\n\n")
plt.xlabel('Model', fontweight ='bold', fontsize = 15) 
plt.ylabel('Accuracy', fontweight ='bold', fontsize = 15)
plt.bar(bar_class,bar_values, width = 0.4)
# Annotating each bar with its value
for i, value in enumerate(bar_values):
    plt.text(i, value, f'{value:.2f}', ha='center', va='bottom', fontweight='bold')
# best algo model is trained aagain 
print(f"Best accuracy  model is {classifi}")
model = make_pipeline(TfidfVectorizer(), classifi)
    
    # Train the model
model.fit(train.data, train.target)

    # Make predictions on the test set
predictions = model.predict(X_test)
    
    # Evaluate the performance of the model
accuracy = accuracy_score(y_test, predictions)
print(f"\nClassifier: {classifi}")
maxx=round(accuracy, 2)
print(f"Accuracy: {accuracy:.2f}")
    
    # Display classification report
print("Classification Report:\n", classification_report(y_test, predictions))
conf_matrix = confusion_matrix(y_test, predictions)
def predict_category(Enter_article, train=train, model=model):
    pred=model.predict([Enter_article])
    return train.target_names[pred[0]]
iface=gr.Interface(fn=predict_category,inputs=gr.Textbox(lines=10, placeholder="Enter text here"),outputs="text", title="Text Classification",description="getting... the categories of Artical/news")
iface.launch(inline=False,share=True)