Update app.py

app.py

@@ -1,29 +1,33 @@
-
-import streamlit as st 
-import torch
-from datasets import load_dataset
-from sentence_transformers import SentenceTransformer, util
-
-embedder = SentenceTransformer('all-mpnet-base-v2')
-
-st.title("iSeBetter : Semantic Transformer")
-st.header("Analyzing Patterns in Text")
-
-
-text_input = st.text_area("Enter the issue details below:")
-
-
-if st.button("Analyse the Issues"):
-    # Perform analysis (your existing code)
-    query_embedding = embedder.encode(text_input, convert_to_tensor=True)
-    corpus_embeddings = torch.load('saved_corpus.pt')
-    corpus_embeddings_name = torch.load('saved_corpus_list.txt')
-    cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
-    top_results = torch.topk(cos_scores, k=5)
-
-    # Results presentation
-    st.subheader("Top 5 Matched Results:")
-    result_table = "<table><tr><th>Matched Text</th><th>Score</th></tr>"
-    for score, idx in zip(top_results[0], top_results[1]):
-        st.markdown(f"- **{corpus_embeddings_name[idx]}** (Score: {score:.4f})")
-        st.progress(score.item())
+import streamlit as st
+import torch
+from sentence_transformers import SentenceTransformer, util
+
+embedder = SentenceTransformer('all-mpnet-base-v2')
+
+st.title("iSeBetter: Semantic Issue Transformer")
+st.caption("Empower Developers with Advanced AI Issue Resolution")
+
+st.markdown('#')
+
+st.subheader("Search for Similar Issues")
+form = st.form("Search for Similar Issues")
+text_input = form.text_area("Please enter the issue details",
+                            help="Title and description of the issue could be a good start!")
+
+if form.form_submit_button():
+    # Perform semantic search
+    query_embedding = embedder.encode(text_input, convert_to_tensor=True)
+    corpus_embeddings = torch.load('saved_corpus.pt')
+    corpus_embeddings_name = torch.load('saved_corpus_list.txt')
+    cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
+    top_results = torch.topk(cos_scores, k=5)
+
+    st.markdown('#')
+    
+    # Display results
+    st.subheader("Top 5 Similar Issues")
+    st.divider()
+    for score, idx in zip(top_results[0], top_results[1]):
+        st.write(corpus_embeddings_name[idx])
+        st.progress(score.item(), f"Score: {score:.4f}")
+        st.divider()