Upload app.py

app.py

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+import gradio as gr
+import wikipedia
+import spacy
+from spacy.lang.en.stop_words import STOP_WORDS
+from string import punctuation
+import nltk
+nltk.download('wordnet', quiet=True)
+from nltk.stem import WordNetLemmatizer
+from heapq import nlargest
+import warnings
+
+
+warnings.filterwarnings("ignore")
+
+def get_wiki_summary(rawtext = input('Requested Topic from Wikipedia    :   ')):
+    text = wikipedia.summary(rawtext)
+    print('\033[1m' + "Original Text Fetched from Wikipedia" + '\033[0m')
+
+    print(text)
+
+    stopwords = list(STOP_WORDS)
+    nlp = spacy.load("en_core_web_sm")
+
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(str(token).lower()) for token in nlp(text) if str(token) not in punctuation and str(token).lower() not in stopwords and len(token) >1]
+    word_counts = {}
+
+    for token in tokens:
+        if token in word_counts.keys():
+            word_counts[token] += 1
+        else:
+            word_counts[token] = 1
+
+        
+
+    sentence_scores = {}
+
+    for sentence in nlp(text).sents:
+        sentence_scores[sentence] = 0
+        for wrd in sentence:
+            if lemmatizer.lemmatize(str(wrd).lower()) in word_counts.keys():
+                sentence_scores[sentence] += word_counts[lemmatizer.lemmatize(str(wrd).lower())]
+
+    summary_length = int(len(sentence_scores)*0.20)
+    summary = str()
+
+    for sentence in nlp(text).sents:
+        for i in range(0,summary_length):
+            if str(sentence).find(str(nlargest(summary_length, sentence_scores, key = sentence_scores.get)[i])) == 0:
+                summary += str(sentence)
+                summary += ' '
+                
+                
+    print('\033[1m' + "Summarized Text" + '\033[0m')
+
+    return print(summary)
+
+gr.Interface(fn=get_wiki_summary, inputs=gr.inputs.Textbox(lines=7, label="Input Text"), outputs="text").launch(inline=False)