import pandas as pd
import plotly.express as px
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from textblob import TextBlob
import spacy
from collections import defaultdict
from collections import Counter
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

nlp = spacy.load('en_core_web_sm')
# Combine all titles into a single string
titles_text = ' '.join(data['Title'])

# Create a WordCloud object
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(titles_text)

# Plot the Word Cloud
fig = px.imshow(wordcloud, title='Word Cloud of Titles')
fig.update_layout(showlegend=False)
fig.show()
data = pd.read_csv("articles.csv", encoding='latin-1')
print(data.head())

# Combine all titles into a single string
titles_text = ' '.join(data['Title'])

# Create a WordCloud object
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(titles_text)

# Plot the Word Cloud
fig = px.imshow(wordcloud, title='Word Cloud of Titles')
fig.update_layout(showlegend=False)
fig.show()

# Sentiment Analysis
data['Sentiment'] = data['Article'].apply(lambda x: TextBlob(x).sentiment.polarity)

# Sentiment Distribution
fig = px.histogram(data, x='Sentiment', title='Sentiment Distribution')
fig.show()

# NER
def extract_named_entities(text):
    doc = nlp(text)
    entities = defaultdict(list)
    for ent in doc.ents:
        entities[ent.label_].append(ent.text)
    return dict(entities)

data['Named_Entities'] = data['Article'].apply(extract_named_entities)

# Visualize NER
entity_counts = Counter(entity for entities in data['Named_Entities'] for entity in entities)
entity_df = pd.DataFrame.from_dict(entity_counts, orient='index').reset_index()
entity_df.columns = ['Entity', 'Count']

fig = px.bar(entity_df.head(10), x='Entity', y='Count', title='Top 10 Named Entities')
fig.show()

# Topic Modeling
# Assuming 'data' is your DataFrame containing the 'Article' column
vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=1000, stop_words='english')
tf = vectorizer.fit_transform(data['Article'])

lda_model = LatentDirichletAllocation(n_components=5, random_state=42)
lda_topic_matrix = lda_model.fit_transform(tf)

# Assign topics to documents
topic_names = ["Topic " + str(i) for i in range(lda_model.n_components)]
data['Dominant_Topic'] = [topic_names[i] for i in lda_topic_matrix.argmax(axis=1)]

# Visualize topics
topic_counts = data['Dominant_Topic'].value_counts().reset_index()
topic_counts.columns = ['Topic', 'Count']  # Renaming columns

# Plotting
fig = px.bar(topic_counts, x='Topic', y='Count', title='Topic Distribution')
fig.show()