Update src/streamlit-app.py with actual code

src/streamlit_app.py

@@ -1,40 +1,130 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from sentence_transformers import SentenceTransformer
+import chromadb
+import time
 
-"""
-# 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])),
-    ))
+# 1. System Configurations
+st.set_page_config(page_title="InsightStream AI", page_icon="⚡", layout="wide")
+
+# Custom CSS to force a high-end dark theme aesthetic
+st.markdown("""
+    <style>
+    .main .block-container {padding-top: 2rem;}
+    .metric-card {background-color: #1e222b; padding: 15px; border-radius: 8px; border: 1px solid #2d3139;}
+    </style>
+""", unsafe_allow_html=True)
+
+# 2. Optimized Resource Initializations
+@st.cache_resource
+def load_analytics_engine():
+    return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+@st.cache_resource
+def init_vector_vault():
+    client = chromadb.Client()
+    return client.get_or_create_collection(
+        name="stream_intelligence_vault",
+        metadata={"hnsw:space": "cosine"}
+    )
+
+engine = load_analytics_engine()
+vault = init_vector_vault()
+
+# Curated high-value corporate intel data
+FEED_PRESETS = [
+    "Market Alert: High structural inflation signals aggressive interest rate hikes next quarter.",
+    "R&D Update: Quantum entanglement framework successfully processing multi-dimensional data arrays.",
+    "Operations Briefing: Deep learning algorithms integrated into production pipelines to automate code audits.",
+    "Infrastructure Notice: Storage systems transitioning entirely to vectorized database mathematical modeling.",
+    "Wellness Policy: New corporate initiatives mandate daily physical breaks and bio-balanced nutrition tracking.",
+    "Logistics Report: Autonomous supply chain mechanisms deployed across regional fulfillment hubs.",
+    "Biotech News: Synthetic neural networks showing unprecedented mastery over biochemical sequencing tasks."
+]
+
+# 3. Main Dashboard Header Layout
+t1, t2 = st.columns([7, 3])
+with t1:
+    st.title("⚡ INSIGHTSTREAM")
+    st.markdown("### *Real-Time Neural Text Processing & Stream Auditing Hub*")
+with t2:
+    st.write("")
+    st.write("")
+    # Dynamic KPI metric cards
+    st.markdown(
+        f'<div class="metric-card">'
+        f'📶 <b>SYSTEM STATUS:</b> <span style="color:#00ffcc;">ACTIVE</span><br>'
+        f'📦 <b>INDEXED DATA STREAMS:</b> {vault.count()}'
+        f'</div>', 
+        unsafe_allow_html=True
+    )
+
+st.divider()
+
+# 4. Tabbed Workplace Navigation (Eliminating the traditional sidebar)
+tab_monitor, tab_ingest = st.tabs(["📊 INTELLIGENCE STREAM MONITOR", "📥 DATA INGESTION ENGINE"])
+
+# --- TAB 1: SEARCH & MONITOR ---
+with tab_monitor:
+    if vault.count() == 0:
+        st.error("🚨 Neural Index Empty: No data feeds detected in the pipeline.")
+        if st.button("⚡ Trigger Auto-Seed Pipeline"):
+            with st.spinner("Processing framework initialization vectors..."):
+                embeddings = engine.encode(FEED_PRESETS).tolist()
+                ids = [f"feed_id_{i}" for i in range(len(FEED_PRESETS))]
+                vault.add(embeddings=embeddings, documents=FEED_PRESETS, ids=ids)
+            st.success("System auto-seeded successfully!")
+            st.rerun()
+    else:
+        # Search controls integrated horizontally into the main screen layout
+        ctrl1, ctrl2 = st.columns([3, 1])
+        with ctrl1:
+            intel_query = st.text_input("🎯 Filter streams via conceptual query:", placeholder="Type a concept, e.g., corporate financial stress or machine learning automation...")
+        with ctrl2:
+            max_nodes = st.select_slider("Max Nodes to Map:", options=[1, 2, 3, 4, 5], value=3)
+
+        if intel_query:
+            with st.spinner("Executing high-dimensional vector search..."):
+                t_start = time.time()
+                query_vec = engine.encode([intel_query]).tolist()
+                payload = vault.query(query_embeddings=query_vec, n_results=max_nodes)
+                latency = (time.time() - t_start) * 1000
+
+            st.markdown(f"##### 🛰️ Stream Analysis Complete (`{latency:.2f}ms latency`)")
+            
+            if payload and payload['documents']:
+                feeds = payload['documents'][0]
+                metrics = payload['distances'][0]
+                node_ids = payload['ids'][0]
+                
+                # Render clean metric-driven grid modules
+                for i in range(len(feeds)):
+                    confidence = (1 - metrics[i]) * 100
+                    
+                    with st.expander(f"🔴 STREAM NODE {node_ids[i]} — Match Confidence: {confidence:.2f}%", expanded=True):
+                        st.write(f"**Data Payload:**")
+                        st.code(feeds[i], language="markdown")
+            else:
+                st.warning("No nodes matched the conceptual threshold.")
+
+# --- TAB 2: DATA INGESTION ---
+with tab_ingest:
+    st.subheader("📥 Raw Text Ingestion Port")
+    st.markdown("Manually inject unstructured text blocks into the vector analytics cluster.")
+    
+    stream_payload = st.text_area("Raw Document Payload:", placeholder="Paste text logs, reports, or data streams here...")
+    
+    if st.button("🚀 Commit Payload to Cluster"):
+        if stream_payload.strip():
+            with st.spinner("Vectorizing input stream..."):
+                assigned_id = f"feed_user_{vault.count() + 1}"
+                payload_vec = engine.encode([stream_payload]).tolist()
+                
+                vault.add(
+                    embeddings=payload_vec,
+                    documents=[stream_payload],
+                    ids=[assigned_id]
+                )
+            st.success(f"Success: Nodes updated. Assigned ID: {assigned_id}")
+            st.rerun()
+        else:
+            st.error("Aborted: Ingestion payload cannot be empty.")