Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,33 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Model name
+MODEL_NAME = "AbdullahAlnemr1/flan-t5-summarizer"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+st.title("FLAN‑T5 Text Summarizer")
+
+input_text = st.text_area("Enter text to summarize:", height=200)
+
+max_new_tokens = st.slider("Max summary length (tokens)", min_value=20, max_value=200, value=100)
+
+if st.button("Generate Summary"):
+    if input_text.strip() == "":
+        st.warning("Please enter some text to summarize.")
+    else:
+        # Tokenize input
+        inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+        # Generate summary
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_new_tokens=max_new_tokens,
+            num_beams=4,
+            early_stopping=True
+        )
+        summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.subheader("Summary:")
+        st.write(summary)