Update app.py

app.py

@@ -1,19 +1,27 @@
 import streamlit as st
 from transformers import pipeline
 
-# Create a text2text-generation pipeline with the "google/flan-t5-base" model
-paraphrase_generator = pipeline("text2text-generation", model="google/flan-t5-base")
+# Create a text generation pipeline
+pipe = pipeline("text-generation", model="ismaelfaro/gpt2-poems.en")
 
-st.title("Paraphrase Generator")
+# Define the Streamlit app
+st.title("Text Generation with Hugging Face Transformers")
 
-user_input = st.text_input("Enter a line:")
+# Input for user to provide a seed text
+seed_text = st.text_input("Enter a seed text:")
 
-if st.button("Generate Paraphrase"):
-    if user_input:
-        # Use a prompt to control the paraphrasing
-        paraphrase_prompt = f"Paraphrase the following line: '{user_input}'"
-        paraphrase = paraphrase_generator(paraphrase_prompt, max_length=100, do_sample=True)[0]["generated_text"]
-        st.markdown("**Paraphrase:**")
-        st.markdown(paraphrase)
+# Button to generate text
+if st.button("Generate Text"):
+    if seed_text:
+        generated_text = pipe(seed_text, max_length=50, do_sample=True)[0]['generated_text']
+        st.write("Generated Text:")
+        st.write(generated_text)
     else:
-        st.warning("Please enter a line.")
+        st.write("Please enter a seed text to generate text.")
+
+# Instructions and information
+st.markdown("This app uses the Hugging Face Transformers library for text generation.")
+st.markdown("Model: ismaelfaro/gpt2-poems.en")
+
+# Provide additional information or options here
+