Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the summarization model (facebook/bart-large-cnn)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Use a smaller grammar correction model
+grammar_correction_pipe = pipeline("text2text-generation", model="pszemraj/grammar-synthesis-small")
+
+# Function for grammar correction
+def correct_grammar(user_input):
+    if user_input.strip():
+        corrected_text = grammar_correction_pipe(user_input)[0]['generated_text']
+        return corrected_text
+    else:
+        return "Please enter some text for grammar correction."
+
+# Function for text summarization
+def summarize_text(user_input):
+    if user_input.strip():
+        summary = summarizer(user_input, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
+        return summary
+    else:
+        return "Please enter some text to summarize."
+
+# Function to combine grammar correction and summarization
+def correct_and_summarize(user_input):
+    corrected_text = correct_grammar(user_input)  # First correct the grammar
+    summary = summarize_text(corrected_text)  # Then summarize the corrected text
+    return summary
+
+# Streamlit UI setup
+st.title("Text Summarization and Grammar Correction Assistant")
+
+# Dropdown to select task
+task = st.selectbox("Choose a task", ["Summarize Text", "Correct Grammar"])
+
+# Input component for text
+user_input = st.text_area("Enter your text here:")
+
+# Button to submit the input
+if st.button("Submit"):
+    if task == "Summarize Text":
+        output = correct_and_summarize(user_input)  # Correct grammar, then summarize
+    elif task == "Correct Grammar":
+        output = correct_grammar(user_input)  # Only correct grammar
+    
+    # Display the result
+    st.text_area("Output", output, height=200)