Update app.py

app.py

@@ -1,27 +1,84 @@
-import spacy
 import streamlit as st
+import nltk
+from nltk.corpus import stopwords
+from nltk.cluster.util import cosine_distance
+import numpy as np
+import networkx as nx
 
-def article_summarizer(article_text, num_sentences=3):
-    nlp = spacy.load("en_core_web_sm")
-    doc = nlp(article_text)
-    sentence_importance = {}
-    for sentence in doc.sents:
-        sentence_tokens = [token for token in sentence if not token.is_stop]
-        sentence_rank = sum(token.rank for token in sentence_tokens)
-        sentence_importance[sentence] = sentence_rank
-    sorted_sentences = sorted(sentence_importance, key=lambda x: sentence_importance[x], reverse=True)
-    summary = " ".join(str(sentence) for sentence in sorted_sentences[:num_sentences])
+# Download NLTK resources
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Function to read and preprocess the article
+def read_article(article):
+    sentences = nltk.sent_tokenize(article)
+    sentences = [sentence for sentence in sentences if len(sentence) > 10]  # Filter out very short sentences
+    return sentences
+
+# Function to compute sentence similarity based on cosine similarity
+def sentence_similarity(sent1, sent2, stopwords):
+    words1 = nltk.word_tokenize(sent1)
+    words2 = nltk.word_tokenize(sent2)
+
+    words1 = [word.lower() for word in words1 if word.isalnum()]
+    words2 = [word.lower() for word in words2 if word.isalnum()]
+
+    all_words = list(set(words1 + words2)
+
+    vector1 = [0] * len(all_words)
+    vector2 = [0] * len(all_words)
+
+    for word in words1:
+        if word in stopwords:
+            continue
+        vector1[all_words.index(word)] += 1
+
+    for word in words2:
+        if word in stopwords:
+            continue
+        vector2[all_words.index(word)] += 1
+
+    return 1 - cosine_distance(vector1, vector2)
+
+# Function to create a similarity matrix of sentences
+def build_similarity_matrix(sentences, stopwords):
+    similarity_matrix = np.zeros((len(sentences), len(sentences))
+
+    for i in range(len(sentences)):
+        for j in range(len(sentences)):
+            if i == j:  # Skip comparing a sentence to itself
+                continue
+            similarity_matrix[i][j] = sentence_similarity(sentences[i], sentences[j], stopwords)
+
+    return similarity_matrix
+
+# Function to generate the article summary
+def generate_summary(article, top_n=5):
+    sentences = read_article(article)
+    stop_words = set(stopwords.words('english'))
+    sentence_similarity_matrix = build_similarity_matrix(sentences, stop_words)
+
+    # Create a graph from the similarity matrix
+    sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_matrix)
+
+    # Use the PageRank algorithm to rank the sentences
+    scores = nx.pagerank(sentence_similarity_graph)
+
+    # Sort the sentences by score
+    ranked_sentences = sorted(((scores[i], sentence) for i, sentence in enumerate(sentences)), reverse=True)
+
+    # Get the top N sentences as the summary
+    summary = " ".join([sentence for _, sentence in ranked_sentences[:top_n]])
     return summary
 
+# Streamlit web app
 st.title("Article Summarizer")
-
-article = st.text_area("Enter your article here:")
-num_sentences = st.slider("Select the number of sentences for the summary:", 1, 10, 3)
+user_article = st.text_area("Enter your article here:")
 
 if st.button("Summarize"):
-    if article:
-        summary = article_summarizer(article, num_sentences)
+    if user_article:
+        summary = generate_summary(user_article)
         st.subheader("Summary:")
         st.write(summary)
     else:
-        st.warning("Please enter an article to summarize.")
+        st.warning("Please enter an article to summarize.")