Update src/streamlit_app.py

src/streamlit_app.py

@@ -14,13 +14,18 @@ MODEL_NAME = "cross-encoder/ms-marco-electra-base"
 MAX_SNIPPET_CHARS = 450
 MAX_SENTENCES = 5
 
+st.logo(
+    image="https://dejan.ai/wp-content/uploads/2024/02/dejan-300x103.png",
+    link="https://dejan.ai/",
+    size="large"
+)
+
 st.set_page_config(
-    page_title="Snippet Generator",
+    page_title="Snippet Generator by DEJAN AI",
     page_icon="✂️",
     layout="centered"
 )
 
-
 @st.cache_resource
 def load_model():
     """Load CrossEncoder model."""
@@ -117,35 +122,26 @@ def generate_snippet(query: str, document: str, model, max_chars: int, max_sents
 
 
 # --- Streamlit UI ---
-st.title("✂️ Snippet Generator")
-st.caption("Recreates Google Vertex AI / Gemini grounding-style snippets")
+st.title("Grounding Snippet Generator", help="cross-encoder/ms-marco-electra-base")
 
 st.markdown("""
-This tool generates extractive snippets from documents using a Cross-Encoder model trained on MS MARCO search relevance data.
-
-**How it works:**
-1. Segments document into sentences
-2. Scores each sentence against your query using `cross-encoder/ms-marco-electra-base`
-3. Selects top-scoring sentences within budget
-4. Stitches them in document order with `...` for gaps
+How much of your page will be used to ground the model for a particular fanout query?
 """)
 
-st.markdown("---")
-
-query = st.text_input("🔍 Query", value="best prostate cancer treatment in the world")
+query = st.text_input("Query", value="best prostate cancer treatment in the world")
 
 document = st.text_area(
-    "📄 Document", 
+    "Web Page Text", 
     height=250,
     placeholder="Paste document content here..."
 )
 
-with st.expander("⚙️ Settings"):
+with st.expander("Settings"):
     max_chars = st.slider("Max snippet characters", 200, 1500, MAX_SNIPPET_CHARS, 50)
     max_sents = st.slider("Max sentences", 2, 15, MAX_SENTENCES)
     show_debug = st.checkbox("Show debug info", value=True)
 
-if st.button("Generate Snippet", type="primary"):
+if st.button("Generate Snippet", help="cross-encoder/ms-marco-electra-base"):
     if query and document:
         with st.spinner("Loading model & scoring sentences..."):
             model = load_model()
@@ -161,6 +157,3 @@ if st.button("Generate Snippet", type="primary"):
                 st.text(f"{score:.4f}: {sent[:80]}...")
     else:
         st.warning("Please enter both a query and document.")
-
-st.markdown("---")
-st.caption("Model: `cross-encoder/ms-marco-electra-base` | [GitHub](https://github.com/UKPLab/sentence-transformers)")