Rollback app.py to previous version

app.py

@@ -1,34 +1,10 @@
-#Import dependencies
 from transformers import pipeline
 import streamlit as st
-from diffusers import DiffusionPipeline
 
-#Text generation pipeline
-#generator = pipeline("text-generation", model="mosaicml/mpt-7b-storywriter", trust_remote_code=True)
-
-#Summarization pipeline
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Image generation pipeline
-#image_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
-
-# Input text
-prompt = st.text_input("Enter a prompt to expand on:")
-
-#Check if text has been entered into the prompt
-if prompt:
-    # Generate text based on the prompt
-    #generated_text = generator(prompt, max_length=300, num_return_sequences=1)
-    #detailed_text = generated_text[0]['generated_text']
-    #st.write("Generated Text:")
-    #st.write(detailed_text)
-
-    # Summarize the generated text
-    summary = summarizer(prompt, max_length=130, min_length=30, do_sample=False)
-    summarized_text = summary[0]['summary_text']
-    st.write("Summarized Text:")
-    st.write(summarized_text)
+textToParaphrase = st.text_input("Need to summarize lengthy text? Paste it here!")
 
-    # Generate an image from the summarized text
-    #image = image_pipe(summarized_text).images[0]
-    #st.image(image, caption="Generated Image")
+if textToParaphrase:
+    summary = summarizer(textToParaphrase, max_length=130, min_length=30, do_sample=False)
+    st.write(summary[0]['summary_text'])  # Displaying the summarized text in Streamlit