Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,43 @@
-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
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    model = AutoModelForSeq2SeqLM.from_pretrained("majorSeaweed/T5-Conv_summarisation_small")
+    tokenizer = AutoTokenizer.from_pretrained("majorSeaweed/T5-Conv_summarisation_small")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Title
+st.title("🧠 Dialogue Summarisation (T5)")
+st.markdown(
+    "This app generates **non-toxic summaries** of multi-turn conversations using a SFT Tuned `t5-small` model."
+)
+
+# Input box
+dialogue = st.text_area(
+    "Paste a conversation below (multi-turn dialogue with speaker turns):",
+    height=200,
+    placeholder="User: Hey, you there?\nBot: Yeah, what's up?\nUser: I just wanted to say you're terrible at this.",
+)
+
+# Generate summary
+if st.button("Generate Summary"):
+    if not dialogue.strip():
+        st.warning("Please enter a conversation.")
+    else:
+        with st.spinner("Generating summary..."):
+            inputs = tokenizer(dialogue, return_tensors="pt", truncation=True, max_length=512)
+            summary_ids = model.generate(
+                **inputs,
+                max_length=60,
+                num_beams=4,
+                early_stopping=True,
+                no_repeat_ngram_size=2
+            )
+            summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+            st.subheader("📝 Summary")
+            st.success(summary)