wehub-resource-sync
324 lines
15 KiB
Python
324 lines
15 KiB
Python
"""
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Agent Memory Layer — Dashboard
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Streamlit UI that connects to the always-on memory agent.
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Visualizes memories, runs queries, and triggers operations.
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Usage:
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# First start the agent:
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python agent.py
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# Then start the dashboard:
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streamlit run dashboard.py
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"""
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import json
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import time
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from pathlib import Path
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import requests
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import streamlit as st
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AGENT_URL = "http://localhost:8888"
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INBOX_DIR = Path("./inbox")
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UPLOAD_EXTENSIONS = [
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"txt", "md", "json", "csv", "log", "xml", "yaml", "yml",
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"png", "jpg", "jpeg", "gif", "webp", "bmp", "svg",
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"mp3", "wav", "ogg", "flac", "m4a", "aac",
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"mp4", "webm", "mov", "avi", "mkv",
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"pdf",
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]
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SAMPLE_TEXTS = [
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{
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"title": "📰 AI Agents in Production",
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"text": (
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"Anthropic released a report showing that 62% of Claude usage is now "
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"code-related, with AI agents being the fastest growing category. "
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"Companies are deploying agents for customer support, code review, "
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"and data analysis. The key challenge remains reliability: agents "
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"fail silently and need human oversight loops."
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),
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},
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{
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"title": "📧 Meeting Notes: Q1 Planning",
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"text": (
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"Discussed Q1 priorities: 1) Ship the new API by March 15, "
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"2) Hire two backend engineers, 3) Reduce inference costs by 40% "
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"by switching to smaller models for routing tasks. Sarah will lead "
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"the API project. Budget approved for $50k in cloud compute."
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),
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},
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{
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"title": "📄 Research: Memory in LLM Systems",
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"text": (
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"Current approaches to LLM memory: 1) Vector databases with RAG: "
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"good for retrieval but no active processing. 2) Conversation "
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"summarization: loses detail over time. 3) Knowledge graphs: "
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"expensive to maintain. The gap: no system actively consolidates "
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"and connects information like human memory does."
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),
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},
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{
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"title": "💡 Product Idea: Smart Inbox",
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"text": (
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"What if email had an AI layer that continuously reads, categorizes, "
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"and summarizes incoming mail? Not just filtering: actually understanding "
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"context across conversations. Competitors: Superhuman (fast UI, no AI "
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"summary), Shortwave (some AI, limited memory)."
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),
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},
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]
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def api_get(path: str) -> dict | None:
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try:
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r = requests.get(f"{AGENT_URL}{path}", timeout=30)
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return r.json()
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except Exception as e:
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st.error(f"Agent not reachable: {e}")
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return None
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def api_post(path: str, data: dict) -> dict | None:
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try:
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r = requests.post(f"{AGENT_URL}{path}", json=data, timeout=60)
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return r.json()
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except Exception as e:
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st.error(f"Agent not reachable: {e}")
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return None
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def render_memory_card(m: dict):
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entities = m.get("entities", [])
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topics = m.get("topics", [])
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connections = m.get("connections", [])
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importance = m.get("importance", 0.5)
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border_color = "#4ade80" if importance >= 0.7 else "#fbbf24" if importance >= 0.4 else "#555"
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st.markdown(
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f"""<div style="border-left: 3px solid {border_color}; padding: 8px 16px;
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margin: 8px 0; background: rgba(255,255,255,0.02); border-radius: 0 8px 8px 0;">
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<div style="display:flex; justify-content:space-between; align-items:center;">
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<strong style="color: #ddd;">Memory #{m['id']}</strong>
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<span style="font-size: 11px; color: #666;">{m.get('created_at', '')[:16]}
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{' | ' + m.get('source', '') if m.get('source') else ''}</span>
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</div>
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<p style="color: #bbb; margin: 8px 0; font-size: 14px;">{m['summary']}</p>
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<div style="display: flex; gap: 6px; flex-wrap: wrap;">
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{''.join(f'<span style="background: rgba(139,92,246,0.15); color: #c4b5fd; padding: 2px 8px; border-radius: 12px; font-size: 11px;">{t}</span>' for t in topics)}
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{''.join(f'<span style="background: rgba(59,130,246,0.15); color: #93c5fd; padding: 2px 8px; border-radius: 12px; font-size: 11px;">{e}</span>' for e in entities[:5])}
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</div>
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{'<div style="margin-top: 6px; font-size: 11px; color: #666;">🔗 ' + str(len(connections)) + ' connections</div>' if connections else ''}
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</div>""",
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unsafe_allow_html=True,
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)
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def main():
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st.set_page_config(page_title="Always On Agent Memory Layer", page_icon="🧠", layout="wide", initial_sidebar_state="expanded")
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st.markdown(
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"""<style>
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.stApp { background-color: #0a0a0f; }
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.stMarkdown { color: #e8e8e8; }
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.stTextInput > div > div > input { background: #12121a; color: #e8e8e8; border-color: #222; }
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.stTextArea > div > div > textarea { background: #12121a; color: #e8e8e8; border-color: #222; }
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section[data-testid="stSidebar"] { background: #08080d; }
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.stat-card { background: rgba(255,255,255,0.03); border: 1px solid rgba(255,255,255,0.06);
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border-radius: 12px; padding: 16px; text-align: center; }
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.stat-number { font-size: 28px; font-weight: 700; color: #c4b5fd; }
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.stat-label { font-size: 11px; color: #666; text-transform: uppercase; letter-spacing: 0.1em; }
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</style>""",
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unsafe_allow_html=True,
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)
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# Sidebar
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with st.sidebar:
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st.markdown("### ⚙️ Agent Status")
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stats = api_get("/status")
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if stats:
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st.markdown(f'<div class="stat-card" style="margin-bottom:8px;"><div class="stat-number" style="color:#4ade80;">●</div><div class="stat-label">Agent Online</div></div>', unsafe_allow_html=True)
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st.markdown("### 📊 Memory Stats")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(f'<div class="stat-card"><div class="stat-number">{stats.get("total_memories", 0)}</div><div class="stat-label">Memories</div></div>', unsafe_allow_html=True)
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with col2:
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st.markdown(f'<div class="stat-card"><div class="stat-number">{stats.get("unconsolidated", 0)}</div><div class="stat-label">Pending</div></div>', unsafe_allow_html=True)
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st.markdown(f'<div class="stat-card" style="margin-top:8px;"><div class="stat-number">{stats.get("consolidations", 0)}</div><div class="stat-label">Consolidations</div></div>', unsafe_allow_html=True)
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else:
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st.markdown(f'<div class="stat-card" style="margin-bottom:8px;"><div class="stat-number" style="color:#ef4444;">●</div><div class="stat-label">Agent Offline</div></div>', unsafe_allow_html=True)
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st.info("Start the agent:\n```\npython agent.py\n```")
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st.markdown("---")
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st.markdown("<p style='text-align: center; color: #555; font-size: 11px; text-transform: uppercase; letter-spacing: 0.15em; margin-bottom: 12px;'>Powered by</p>", unsafe_allow_html=True)
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logo_col1, logo_col2 = st.columns(2)
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with logo_col1:
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st.image("docs/Gemini_logo.png", use_container_width=True)
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with logo_col2:
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st.image("docs/adk_logo.png", width=90)
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st.caption(f"Endpoint: `{AGENT_URL}`")
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# Main
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st.markdown(
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"""<div style="text-align: center; padding: 20px 0 10px;">
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<span style="font-size: 48px;">🧠</span>
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<h1 style="background: linear-gradient(to right, #c4b5fd, #93c5fd);
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-webkit-background-clip: text; -webkit-text-fill-color: transparent;
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font-size: 36px; margin: 8px 0 4px;">Always On Agent Memory Layer</h1>
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<p style="color: #666; font-size: 14px; max-width: 600px; margin: 0 auto;">
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Always-on memory agent that processes, consolidates, and connects information.<br>
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Built with <strong style="color: #93c5fd;">Google ADK</strong> + <strong style="color: #c4b5fd;">Gemini 3.1 Flash-Lite</strong>.
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Runs 24/7 as a background process.
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</p>
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</div>""",
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unsafe_allow_html=True,
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)
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tab_ingest, tab_query, tab_memories = st.tabs(["📥 Ingest", "🔍 Query", "🧠 Memory Bank"])
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with tab_ingest:
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st.markdown("#### Feed information into memory")
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st.markdown("<p style='color: #666; font-size: 13px;'>Paste text or drop files in the <code>./inbox</code> folder. The <strong>IngestAgent</strong> processes everything automatically.</p>", unsafe_allow_html=True)
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input_text = st.text_area("Input", height=150, placeholder="Paste text here...", label_visibility="collapsed")
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col_ingest, col_samples = st.columns([1, 1])
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with col_ingest:
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if st.button("⚡ Process into Memory", type="primary", use_container_width=True):
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if input_text.strip():
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with st.spinner("IngestAgent processing..."):
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t0 = time.time()
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result = api_post("/ingest", {"text": input_text, "source": "dashboard"})
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elapsed = time.time() - t0
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if result:
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st.success(f"Processed in {elapsed:.1f}s")
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st.markdown(result.get("response", ""))
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with col_samples:
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st.markdown("<p style='color: #555; font-size: 12px;'>Or try a sample:</p>", unsafe_allow_html=True)
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for s in SAMPLE_TEXTS:
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if st.button(s["title"], use_container_width=True):
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with st.spinner(f"IngestAgent processing..."):
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t0 = time.time()
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result = api_post("/ingest", {"text": s["text"], "source": s["title"]})
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elapsed = time.time() - t0
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if result:
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st.success(f"**{s['title']}** processed in {elapsed:.1f}s")
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st.markdown(result.get("response", ""))
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st.markdown("---")
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st.markdown("#### 📎 Upload Files")
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st.markdown("<p style='color: #666; font-size: 13px;'>Upload images, audio, video, PDFs, or text files. "
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"They'll be saved to <code>./inbox</code> and processed automatically by the agent.</p>",
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unsafe_allow_html=True)
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uploaded_files = st.file_uploader(
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"Drop files here",
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type=UPLOAD_EXTENSIONS,
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accept_multiple_files=True,
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label_visibility="collapsed",
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)
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if uploaded_files:
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INBOX_DIR.mkdir(parents=True, exist_ok=True)
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for uf in uploaded_files:
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dest = INBOX_DIR / uf.name
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if dest.exists():
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st.warning(f"**{uf.name}** already exists in inbox, skipping.")
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continue
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dest.write_bytes(uf.getvalue())
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ext = Path(uf.name).suffix.lower()
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if ext in {".png", ".jpg", ".jpeg", ".gif", ".webp", ".bmp"}:
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icon = "🖼️"
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elif ext in {".mp3", ".wav", ".ogg", ".flac", ".m4a", ".aac"}:
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icon = "🎵"
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elif ext in {".mp4", ".webm", ".mov", ".avi", ".mkv"}:
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icon = "🎬"
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elif ext == ".pdf":
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icon = "📑"
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else:
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icon = "📄"
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st.success(f"{icon} **{uf.name}** saved to inbox — agent will process it shortly.")
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st.markdown("---")
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st.markdown("#### 🔄 Consolidate Memories")
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st.markdown("<p style='color: #666; font-size: 13px;'>The <strong>ConsolidateAgent</strong> runs automatically every 30 minutes. Trigger it manually here.</p>", unsafe_allow_html=True)
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if st.button("🔄 Run Consolidation", use_container_width=True):
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with st.spinner("ConsolidateAgent processing..."):
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t0 = time.time()
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result = api_post("/consolidate", {})
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elapsed = time.time() - t0
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if result:
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st.success(f"Consolidated in {elapsed:.1f}s")
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st.markdown(result.get("response", ""))
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with tab_query:
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st.markdown("#### Ask your memory anything")
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st.markdown("<p style='color: #666; font-size: 13px;'>The <strong>QueryAgent</strong> searches all memories and synthesizes answers with citations.</p>", unsafe_allow_html=True)
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question = st.text_input("Question", placeholder="What do you know about AI agents?", label_visibility="collapsed")
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sample_qs = [
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"What are the main themes across everything you remember?",
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"What connections do you see between different memories?",
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"What should I focus on based on what you know?",
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"Summarize everything in 3 bullet points.",
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]
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cols = st.columns(2)
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for i, sq in enumerate(sample_qs):
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with cols[i % 2]:
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if st.button(f"💬 {sq}", use_container_width=True):
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question = sq
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if question:
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with st.spinner("QueryAgent searching memory..."):
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t0 = time.time()
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result = api_get(f"/query?q={question}")
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elapsed = time.time() - t0
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if result:
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st.markdown(
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f"""<div style="background: rgba(139,92,246,0.05); border: 1px solid rgba(139,92,246,0.15);
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border-radius: 12px; padding: 20px; margin: 16px 0;">
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<span style="font-size: 12px; color: #a78bfa;">{elapsed:.1f}s</span>
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<div style="color: #ddd; line-height: 1.7; margin-top: 8px;">{result.get('answer', '')}</div>
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</div>""",
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unsafe_allow_html=True,
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)
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with tab_memories:
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st.markdown("#### Stored Memories")
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data = api_get("/memories")
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if data and data.get("memories"):
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for m in data["memories"]:
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col_card, col_del = st.columns([10, 1])
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with col_card:
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render_memory_card(m)
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with col_del:
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if st.button("🗑️", key=f"del_{m['id']}", help=f"Delete memory #{m['id']}"):
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result = api_post("/delete", {"memory_id": m["id"]})
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if result and result.get("status") == "deleted":
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st.toast(f"Deleted memory #{m['id']}")
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st.rerun()
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st.markdown("---")
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with st.expander("⚠️ Danger Zone"):
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st.markdown("<p style='color: #ef4444; font-size: 13px;'>This will permanently delete all memories, consolidations, processed file history, <strong>and all files in the inbox folder</strong>.</p>", unsafe_allow_html=True)
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if st.button("🗑️ Clear All Memories", type="primary", use_container_width=True):
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result = api_post("/clear", {})
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if result:
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files_del = result.get("files_deleted", 0)
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msg = f"Cleared {result.get('memories_deleted', 0)} memories"
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if files_del:
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msg += f" and {files_del} inbox files"
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st.toast(msg)
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st.rerun()
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else:
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st.info("No memories yet. Ingest some information or drop files in ./inbox")
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if __name__ == "__main__":
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main()
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