Update app.py

app.py

@@ -16,9 +16,8 @@ if "messages" not in st.session_state:
     st.session_state.messages = []
 if "query" not in st.session_state:
     st.session_state.query = ""
-
-# Load the banner image from the same directory
-st.image("banner_policy.jpg", use_container_width=True)
+if "extended_query" not in st.session_state:
+    st.session_state.extended_query = ""
 
 ##############################################
 ##
@@ -77,19 +76,6 @@ def get_credentials():
 from ibm_watsonx_ai.client import APIClient
 from ibm_watsonx_ai.foundation_models.embeddings.sentence_transformer_embeddings import SentenceTransformerEmbeddings
 
-if "client" not in st.session_state:
-    with st.spinner("⏳ Waking the wizard ..."):
-        IBM_token()
-        wml_credentials = get_credentials()
-        st.session_state.client = APIClient(credentials=wml_credentials, project_id=os.getenv("IBM_PROJECT_ID"))
-        
-        #vector_index_details = st.session_state.client.data_assets.get_details(VECTOR_DB)
-        #st.session_state.vector_index_properties = vector_index_details["entity"]["vector_index"]
-        #st.session_state.top_n = 20 if st.session_state.vector_index_properties["settings"].get("rerank") else int(st.session_state.vector_index_properties["settings"]["top_k"])
-        
-        st.session_state.emb = SentenceTransformerEmbeddings('sentence-transformers/all-MiniLM-L6-v2')
-        st.session_state.top_n = 10
-
 def rerank( client, documents, query, top_n ):
     from ibm_watsonx_ai.foundation_models import Rerank
 
@@ -176,10 +162,6 @@ def hydrate_chromadb():
     )
     return collection
 
-if "chroma_collection" not in st.session_state:
-    with st.spinner("⏳ Dusting off the scroll books ..."):
-        st.session_state.chroma_collection = hydrate_chromadb()
-
 def proximity_search( question ):
     query_vectors = st.session_state.emb.embed_query(question)
     query_result = st.session_state.chroma_collection.query(
@@ -213,12 +195,16 @@ def do_query(query):
     #st.session_state.messages.append({"role": "assistant", "content": assistant_reply})
     #st.session_state.query = query
 
-    
-# Streamlit UI
-st.title("🔍 Policy Scroll")
-st.subheader("AI-Powered Project & Policy Matching")
-st.write("Explore the Lab Lab Library to find relevant past projects that align with your policy or new initiative.")
+############################
+##
+##   UI
+##
+############################
+
+# Load the banner image from the same directory
+st.image("banner_policy.jpg", use_container_width=True)
 
+# set up sidebar
 st.sidebar.title("🔍 Synergy Scrolling")
 st.sidebar.write(
     "Synergy Scrolling analyzes policies and finds relevant past projects. "
@@ -226,8 +212,52 @@ st.sidebar.write(
     "previous LabLab hackathons."
 )
 
+################ INIT
+
+if "client" not in st.session_state:
+    with st.spinner("⏳ Waking the wizard ..."):
+        IBM_token()
+        wml_credentials = get_credentials()
+        st.session_state.client = APIClient(credentials=wml_credentials, project_id=os.getenv("IBM_PROJECT_ID"))
+        
+        #vector_index_details = st.session_state.client.data_assets.get_details(VECTOR_DB)
+        #st.session_state.vector_index_properties = vector_index_details["entity"]["vector_index"]
+        #st.session_state.top_n = 20 if st.session_state.vector_index_properties["settings"].get("rerank") else int(st.session_state.vector_index_properties["settings"]["top_k"])
+        
+        st.session_state.emb = SentenceTransformerEmbeddings('sentence-transformers/all-MiniLM-L6-v2')
+        st.session_state.top_n = 10
+
+if "chroma_collection" not in st.session_state:
+    with st.spinner("⏳ Dusting off the scroll books ..."):
+        st.session_state.chroma_collection = hydrate_chromadb()
+
 query = ""
 
+################ main UI
+
+st.title("🔍 Policy Scroll")
+st.subheader("AI-Powered Project & Policy Matching")
+st.write("Explore the Lab Lab Library to find relevant past projects that align with your policy or new initiative.")
+
+################ sidebar UI
+
+policy_input = st.sidebar.text_area("📝 Enter Your Policy or Business Idea:")
+
+if st.sidebar.button("🔗 Connect with IBM Chat"):
+    if policy_input.strip():
+        prompt = f"Define search criteria for projects to implement: {policy_input}"
+        
+        # Get response from IBM
+        with st.spinner("Analyzing..."):
+            result = IBM_chat(prompt)
+            query = "Find 3 projects that best match these criteria: " + result
+            st.session_state["extended_query"] = query
+    else:
+        st.sidebar.warning("Please enter a policy or business idea first!")
+
+# Display AI result in another textarea
+st.sidebar.text_area("💡 Extended query:", value=st.session_state.get("extended_query", ""), height=150)
+
 # Suggested search queries as buttons
 col1, col2, col3 = st.columns(3)