Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,55 @@
-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
+
+# ---- Configuration ----
+MODEL_NAME = "AbdullahAlnemr1/flan-t5-summarizer"  
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ---- Load model and tokenizer ----
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float32)
+    model.to(device)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# ---- Streamlit App ----
+st.title("Text Summarizer")
+st.write("Generate concise summariy.")
+
+# ---- Input Area ----
+article = st.text_area("Enter the article to summarize:", height=250)
+
+# ---- Parameters ----
+max_input_len = 512
+max_output_len = 150
+
+# ---- Generate Summary ----
+if st.button("Generate Summary"):
+    if not article.strip():
+        st.warning("Please enter some text to summarize.")
+    else:
+        with st.spinner("Generating summary..."):
+            inputs = tokenizer(
+                article,
+                return_tensors="pt",
+                max_length=max_input_len,
+                truncation=True
+            ).to(device)
+
+            summary_ids = model.generate(
+                **inputs,
+                max_length=max_output_len,
+                num_beams=4,
+                length_penalty=2.0,
+                early_stopping=True
+            )
+
+            summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+        # ---- Output ----
+        st.subheader("Generated Summary:")
+        st.write(summary)