Created App

app.py

@@ -1,38 +1,25 @@
 import streamlit as st
 from transformers import pipeline
 
-# Title of the app
-st.title("Hugging Face Transformers with Streamlit")
+# Initialize the summarizer pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Sidebar for selecting model
-st.sidebar.header("Select a Model")
+def summarize_text(text):
+    summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
 
-model_option = st.sidebar.radio("Choose a task", ["Text Generation", "Text Summarization", "Sentiment Analysis"])
+# Streamlit app layout
+st.title("Text Summarizer")
+st.write("This app uses Hugging Face's transformers to summarize any text you provide.")
 
-# Load the transformer model based on selected task
-if model_option == "Text Generation":
-    st.header("Text Generation")
-    model = pipeline("text-generation", model="gpt2")
-    user_input = st.text_area("Enter your prompt:", "Once upon a time")
-    
-    if st.button("Generate Text"):
-        result = model(user_input, max_length=100, num_return_sequences=1)
-        st.write(result[0]["generated_text"])
+# User input
+input_text = st.text_area("Enter Text to Summarize", height=200)
 
-elif model_option == "Text Summarization":
-    st.header("Text Summarization")
-    model = pipeline("summarization", model="facebook/bart-large-cnn")
-    user_input = st.text_area("Enter the text to summarize:", "The quick brown fox jumps over the lazy dog.")
-    
-    if st.button("Summarize Text"):
-        result = model(user_input, min_length=25, max_length=100, length_penalty=2.0, num_beams=4, early_stopping=True)
-        st.write(result[0]["summary_text"])
-
-elif model_option == "Sentiment Analysis":
-    st.header("Sentiment Analysis")
-    model = pipeline("sentiment-analysis")
-    user_input = st.text_area("Enter the text to analyze:", "I love programming!")
-    
-    if st.button("Analyze Sentiment"):
-        result = model(user_input)
-        st.write(f"Sentiment: {result[0]['label']}, Confidence: {result[0]['score']:.2f}")
+if st.button("Summarize"):
+    if input_text:
+        with st.spinner("Summarizing..."):
+            summary = summarize_text(input_text)
+            st.subheader("Summary:")
+            st.write(summary)
+    else:
+        st.warning("Please enter some text to summarize.")