app python file

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load Hugging Face summarization model
+@st.cache_resource
+def load_summarization_model():
+    summarizer = pipeline("summarization", model="Ramji/bart-cn-large-medical-summary")  # You can replace with another model
+    return summarizer
+
+summarizer = load_summarization_model()
+
+# Streamlit UI
+st.title("Text Summarization with Hugging Face")
+
+st.write("This app uses a Finetuned Bart model to summarize long text inputs.")
+
+# Input text area for the user
+user_input = st.text_area("Enter text to summarize:", height=300)
+
+# Set summary parameters (optional)
+max_length = st.slider("Maximum Summary Length", 30, 500, 150)
+min_length = st.slider("Minimum Summary Length", 10, 100, 30)
+
+# Summarize when the button is clicked
+if st.button("Summarize"):
+    if user_input:
+        # Get the summary
+        summary = summarizer(user_input, max_length=max_length, min_length=min_length, do_sample=False)
+        # Display the result
+        st.subheader("Summary")
+        st.write(summary[0]['summary_text'])
+    else:
+        st.write("Please enter some text to summarize.")