Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled11.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Y2vv_pZ5nKXKLrXrmsSu6z8hz6ncjWOz
+"""
+
+import streamlit as st
+from transformers import BartForConditionalGeneration, BartTokenizer
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from nltk.probability import FreqDist
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+st.title("NLP Text Analyzer")
+
+user_input = st.text_area("Enter your text:", "Type here...")
+
+if user_input:
+    st.header("Summary:")
+    
+    # Load pre-trained BART model and tokenizer
+    model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
+    tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
+
+    # Tokenize the input text
+    inputs = tokenizer.encode("summarize: " + user_input, return_tensors="pt", max_length=1024, truncation=True)
+
+    # Generate the summary
+    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    
+    st.write(summary)
+
+   # Your previous code for creating the Word Cloud plot
+    st.header("Word Cloud:")
+    wordcloud = WordCloud(stopwords=set(stopwords.words('english')), background_color='white').generate(user_input)
+    plt.figure(figsize=(8, 6))  # Adjust the figsize as needed
+    plt.imshow(wordcloud, interpolation='bilinear')
+    plt.axis("off")
+
+# Display the Word Cloud plot using st.pyplot() with the explicit figure object
+    st.pyplot(plt.gcf())
+
+    st.header("Most Common Words:")
+    words = word_tokenize(user_input)  # Tokenize the user input text
+    fdist = nltk.FreqDist(words)
+    most_common_words = fdist.most_common(10)
+
+    # Prepare data for tabular format
+    data = {
+        "Word": [word[0] for word in most_common_words],
+        "Frequency": [word[1] for word in most_common_words]
+    }
+
+    # Display as a table
+    st.table(data)