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app.py

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+import streamlit as st
+import base64
+from model import Model
+
+st.title("Text Summarizer")
+
+with st.form(key="clf_form"):
+    text_input = st.text_area("Type Here:")
+    submit_btn = st.form_submit_button(label="Submit")
+
+    count_of_words = len(text_input.split())
+
+    if submit_btn:
+        if text_input == "":
+            st.error("Enter something in order to summarize it.", icon="⛔️")
+        elif count_of_words <= 100:
+            st.warning("Please enter more than 100 words in order to summarize it.", icon="⚠️")
+        else:
+            st.subheader("Output:")
+
+            col1, col2 = st.columns(2)
+
+            output = Model.predict(text=text_input)
+
+            with col1:
+                st.info("Original Text:")
+                st.write(text_input)
+
+            with col2:
+                st.info("Summarized Text:")
+                st.write(output)

model.py

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+import re
+import spacy
+from heapq import nlargest
+class Model():
+
+    try:
+        nlp = spacy.load("en_core_web_sm")
+    except OSError:
+        import subprocess
+        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+    def predict(text):
+        stop_words = [ 'stop', 'the', 'to', 'and', 'a', 'in', 'it', 'is', 'I', 'that', 'had', 'on', 'for', 'were', 'was']
+        nlp = spacy.load("en_core_web_sm")
+        doc = nlp(text)
+    
+        lemmatized_text = " ".join([token.lemma_ for token in doc])
+    
+        re_text = re.sub("[^\s\w,.]"," ",lemmatized_text)
+        re_text = re.sub("[ ]{2,}"," ",re_text).lower()
+    
+        word_frequencies = {}
+        for word in doc:
+            if word.text not in "\n":
+                if word.text not in stop_words:
+                    if word.text not in word_frequencies.keys():
+                        word_frequencies[word.text] = 1
+                    else:
+                        word_frequencies[word.text] +=1
+    
+        max_word_frequency = max(word_frequencies.values(),default=0)
+    
+        for word in word_frequencies.keys():
+            word_frequencies[word] = word_frequencies[word] / max_word_frequency
+    
+        sent_tokens = [sent for sent in doc.sents]
+        sent_scores = {}
+    
+        for sent in sent_tokens:
+            for word in sent:
+                if word.text in word_frequencies.keys():
+                    if sent not in sent_scores.keys():
+                        sent_scores[sent] = word_frequencies[word.text]
+                    else:
+                        sent_scores[sent] += word_frequencies[word.text]
+       
+        sentence_length = int(len(sent_tokens)*0.3)
+        summary = nlargest(sentence_length,sent_scores,sent_scores.get)
+        final_summary = [word.text for word in summary]
+        final_summary = " ".join(final_summary)
+        return final_summary

requirements.txt

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+spacy==3.5.2
+pandas==2.0.1
+regex==2023.5.5
+streamlit==1.22.0