Added TextSummarization Model, Continuation to PR #785

Project-Structure.md

@@ -683,6 +683,8 @@
     * [Info](Web_Development/Movie_Recommendation/info.py)
     * [Pythonapplication](Web_Development/Movie_Recommendation/pythonApplication.py)
     * [Test](Web_Development/Movie_Recommendation/test.py)
+  * Test Sumarrization Nlp
+    * [Textsummary](Web_Development/Test_Sumarrization_NLP/textsummary.py)
   * Chatbot
     * [App](Web_Development/chatbot/app.py)
     * [Chat](Web_Development/chatbot/chat.py)

Web_Development/Test_Sumarrization_NLP/README.md

@@ -0,0 +1,28 @@
+## **Text Summarization NLP MOdel**
+
+A text summarization NLP model where user and type their huge paragraphs and the model will summarize it
+
+
+### 🎯 **Goal**
+
+Python Project - People do face issues to summarize huge texts or students do face issues to learn huge texts. this model helps users to summarize those texts and use it according to their need, for example a learner can easily learn faster and understand the summarized text instead of going through huge paragraphs
+
+Modules Used:
+    1. Transformers
+    2. Streamlit
+    3. tf-keras
+
+
+# MOdel Deployment with Streamlit
+How to deploy:
+- Deploy with Streamlit
+
+## Deployment:
+
+streamlit run textsummarizer.py
+
+
+### ✒ *Your Signature*
+
+Ankan Mukhopadhyay
+[GitHub Profile](https://github.com/Peart-Guy) | [LinkedIn](https://www.linkedin.com/in/ankan-mukhopadhyaypeartguy/)

Web_Development/Test_Sumarrization_NLP/textsummary.py

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+import streamlit as st
+from transformers import pipeline
+
+# Title of the web app
+st.title("Text Summarization Tool")
+
+# Load the summarization model
+@st.cache_resource(show_spinner=True)  # Cache the model loading for faster performance
+def load_summarizer():
+    return pipeline("summarization", model="t5-small")
+
+summarizer = load_summarizer()
+
+# Instructions for users
+st.write("Enter the text you'd like to summarize (minimum 50 words).")
+
+# Create a text area for the user to input text
+user_input = st.text_area("Input Text", height=200)
+
+# A button to initiate the summarization process
+if st.button("Summarize"):
+    if len(user_input.split()) < 50:
+        st.warning("Please enter at least 50 words for summarization.")
+    else:
+        # Show a spinner while the summarization is being processed
+        with st.spinner("Summarizing..."):
+            # Generate the summary
+            summary = summarizer(user_input, max_length=150, min_length=30, do_sample=False)
+            # Display the summarized text
+            st.subheader("Summary:")
+            st.write(summary[0]['summary_text'])