Create app.py

app.py

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+import nltk
+from nltk.corpus import stopwords
+from nltk.cluster.util import cosine_distance
+import numpy as np
+import networkx as nx
+
+def read_para(string):
+    article = string.split(". ")
+    sentences = []
+    for sentence in article:
+        sentences.append(sentence.replace("[^a-zA-Z]", " ").split(" "))
+    sentences.pop() 
+    return sentences
+
+def similarity_in_sentences(sent1, sent2, stopwords=None):
+    if stopwords is None:
+        stopwords = []
+ 
+    sent1 = [w.lower() for w in sent1]
+    sent2 = [w.lower() for w in sent2]
+ 
+    all_words = list(set(sent1 + sent2))
+ 
+    vector1 = [0] * len(all_words)
+    vector2 = [0] * len(all_words)
+    for w in sent1:
+        if w in stopwords:
+            continue
+        vector1[all_words.index(w)] += 1
+    for w in sent2:
+        if w in stopwords:
+            continue
+        vector2[all_words.index(w)] += 1
+    return 1 - cosine_distance(vector1, vector2)
+
+def build_similarity_matrix(sentences, stop_words):
+    similarity_matrix = np.zeros((len(sentences), len(sentences)))
+    for w1 in range(len(sentences)):
+        for w2 in range(len(sentences)):
+            if w1 == w2: 
+                continue 
+            similarity_matrix[w1][w2] = similarity_in_sentences(sentences[w1], sentences[w2], stop_words)
+    return similarity_matrix
+
+def summary(txt, top_n=5):
+    nltk.download("stopwords")
+    stop_words = stopwords.words('english')
+    summarized_text = []
+    sentences =  read_para(txt)
+    sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)
+    sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
+    scores = nx.pagerank(sentence_similarity_graph)
+    ranked_sentence = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)    
+    for i in range(top_n):
+        summarized_text.append(" ".join(ranked_sentence[i][1]))
+    return(". ".join(summarized_text))
+
+
+    
+import streamlit as st
+st.title("Text Summarizer using Streamlit")
+file = st.file_uploader("Upload file", type=["txt"])
+no_para=st.text_input("Enter the size of summarized paragraph :")
+if file is not None:
+    if no_para is not None and len(no_para)>0:
+        content = file.read().decode("utf-8")
+        st.subheader("Summarized Text : ")
+        st.markdown(summary(content,int(no_para)))