Create app.py

app.py

@@ -0,0 +1,55 @@
+import streamlit as st
+from transformers import pipeline
+
+# Streamlit App
+st.set_page_config(page_title="T5 FineTuning Summarizer", layout="centered")
+# Load the summarization pipeline
+@st.cache_resource
+def load_model():
+    return pipeline("text2text-generation", model="trohith89/KDTS_T5_Summary_FineTune")
+
+pipe = load_model()
+
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+        .main {
+            background-color: #f0f2f6;
+        }
+        .stTextArea textarea {
+            height: 300px !important;
+            font-size: 16px;
+        }
+        .headline {
+            font-size: 36px;
+            font-weight: bold;
+            text-align: center;
+            color: #4B8BBE;
+            padding: 20px;
+        }
+        .stButton>button {
+            background-color: #4B8BBE;
+            color: white;
+            font-size: 18px;
+            padding: 10px 20px;
+            border-radius: 8px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Headline
+st.markdown('<div class="headline">T5 FineTuning Summarizer</div>', unsafe_allow_html=True)
+
+# Text input
+user_input = st.text_area("Enter your long text below:", height=300, placeholder="Paste or type your content here...")
+
+# Summarize button
+if st.button("Summarize"):
+    if user_input.strip():
+        with st.spinner("Generating summary..."):
+            summary = pipe(user_input, max_length=150, min_length=30, do_sample=False)[0]['generated_text']
+        st.subheader("📝 Summary:")
+        st.success(summary)
+    else:
+        st.warning("Please enter some text to summarize.")