streamlit_app.py

#!/usr/bin/env python3
"""Ghost Malone: MCP-powered emotional intelligence chatbot - Streamlit version"""

import json
import asyncio
import os
from dotenv import load_dotenv
import streamlit as st
import plotly.graph_objects as go

from utils.orchestrator import get_orchestrator

load_dotenv()

# Page config
st.set_page_config(
    page_title="Ghost Malone",
    page_icon="👻",
    layout="wide"
)

# Clear memory on startup for fresh conversations
if os.path.exists("memory.json"):
    os.remove("memory.json")
    print("🧹 Cleared previous memory for fresh start")


@st.cache_resource
def get_event_loop():
    """Create and cache event loop."""
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    return loop


@st.cache_resource
def boot_orchestrator():
    """Bootstrap the orchestrator with all MCP servers."""
    loop = get_event_loop()
    orchestrator = loop.run_until_complete(get_orchestrator())
    print("🧰 Ghost Malone orchestrator initialized")
    return orchestrator


def run_async(coro):
    """Run async coroutine in the persistent event loop."""
    loop = get_event_loop()
    return loop.run_until_complete(coro)


def create_emotion_plot(emotion_arc):
    """Create a Plotly scatter plot showing emotions on valence/arousal grid."""
    if not emotion_arc or not emotion_arc.get("trajectory"):
        # Empty plot with quadrant labels
        fig = go.Figure()
        fig.add_trace(
            go.Scatter(
                x=[0],
                y=[0.5],
                mode="text",
                text=["No emotional data yet"],
                textfont=dict(size=14, color="gray"),
                showlegend=False,
            )
        )
        fig.update_layout(
            title="Emotional Trajectory (Russell's Circumplex)",
            xaxis=dict(range=[-1, 1], title="Valence (negative ← → positive)", zeroline=True),
            yaxis=dict(range=[0, 1], title="Arousal (calm ← → activated)", zeroline=True),
            height=400,
            template="plotly_white",
        )
        return fig

    trajectory = emotion_arc["trajectory"]
    x_vals = [p["valence"] for p in trajectory]
    y_vals = [p["arousal"] for p in trajectory]
    labels = [p["labels"] for p in trajectory]

    fig = go.Figure()

    # Plot trajectory with numbered markers
    fig.add_trace(
        go.Scatter(
            x=x_vals,
            y=y_vals,
            mode="lines+markers+text",
            marker=dict(size=12, color="purple", line=dict(width=2, color="white")),
            line=dict(color="purple", width=2),
            text=[str(i + 1) for i in range(len(x_vals))],
            textposition="top center",
            textfont=dict(size=10, color="white"),
            hovertemplate="<b>Message %{text}</b><br>Valence: %{x:.2f}<br>Arousal: %{y:.2f}<br>Emotions: %{customdata}<extra></extra>",
            customdata=labels,
            showlegend=False,
        )
    )

    # Add quadrant labels
    quadrant_labels = [
        dict(x=0.5, y=0.75, text="High Arousal<br>Positive", color="green"),
        dict(x=-0.5, y=0.75, text="High Arousal<br>Negative", color="red"),
        dict(x=0.5, y=0.25, text="Low Arousal<br>Positive", color="lightgreen"),
        dict(x=-0.5, y=0.25, text="Low Arousal<br>Negative", color="pink"),
    ]

    for label in quadrant_labels:
        fig.add_annotation(
            x=label["x"],
            y=label["y"],
            text=label["text"],
            showarrow=False,
            font=dict(size=11, color=label["color"]),
            bgcolor="rgba(255,255,255,0.7)",
            borderpad=4,
        )

    fig.update_layout(
        title="Emotional Trajectory (Russell's Circumplex)",
        xaxis=dict(range=[-1, 1], title="Valence (negative ← → positive)", zeroline=True),
        yaxis=dict(range=[0, 1], title="Arousal (calm ← → activated)", zeroline=True),
        height=400,
        template="plotly_white",
    )

    return fig


async def process_message(user_message, min_confidence, min_arousal, min_depth):
    """Process user message through the MCP pipeline."""
    orchestrator = boot_orchestrator()

    # Run the emotional pipeline
    result = await orchestrator.call_tool(
        "run_emotional_pipeline",
        {
            "user_input": user_message,
            "intervention_config": {
                "min_confidence": min_confidence,
                "min_arousal": min_arousal,
                "min_depth": min_depth,
            },
        },
    )

    # Parse results
    content = result.content[0].text if result.content else "{}"
    data = json.loads(content)

    return {
        "response": data.get("response", "I'm processing your message..."),
        "emotion": data.get("emotion", {}),
        "need": data.get("need", {}),
        "intervention": data.get("intervention"),
        "emotion_arc": data.get("emotion_arc", {}),
    }


# Initialize session state
if "messages" not in st.session_state:
    st.session_state.messages = []

# Title
st.title("👻 Ghost Malone")
st.markdown("*Emotion-aware MCP chatbot with constitutional alignment*")

# Sidebar controls
with st.sidebar:
    st.header("⚙️ Intervention Settings")
    st.markdown("Control when Ghost Malone offers interventions:")

    min_confidence = st.slider(
        "Confidence Threshold",
        min_value=0.0,
        max_value=1.0,
        value=0.7,
        step=0.05,
        help="Minimum confidence in emotion detection"
    )

    min_arousal = st.slider(
        "Arousal Threshold",
        min_value=0.0,
        max_value=1.0,
        value=0.4,
        step=0.05,
        help="Minimum emotional arousal level"
    )

    min_depth = st.slider(
        "Conversation Depth",
        min_value=1,
        max_value=5,
        value=2,
        step=1,
        help="Minimum number of messages before interventions"
    )

    st.markdown("---")
    if st.button("🧹 Clear History"):
        st.session_state.messages = []
        if os.path.exists("memory.json"):
            os.remove("memory.json")
        st.rerun()

# Main chat area
col1, col2 = st.columns([2, 1])

with col1:
    st.subheader("💬 Chat")

    # Display chat history
    for msg in st.session_state.messages:
        with st.chat_message(msg["role"]):
            st.markdown(msg["content"])

            # Show emotion/need for user messages
            if msg["role"] == "user" and "metadata" in msg:
                meta = msg["metadata"]
                if meta.get("emotion"):
                    st.caption(f"🎭 {meta['emotion'].get('labels', '')} (valence: {meta['emotion'].get('valence', 0):.2f}, arousal: {meta['emotion'].get('arousal', 0):.2f})")
                if meta.get("need"):
                    st.caption(f"🎯 Need: {meta['need'].get('primary_need', '')} ({meta['need'].get('confidence', 0):.2f})")

            # Show intervention for assistant messages
            if msg["role"] == "assistant" and "intervention" in msg:
                if msg["intervention"]:
                    with st.expander("💡 Intervention Offered"):
                        st.markdown(msg["intervention"])

    # Chat input
    if prompt := st.chat_input("How are you feeling?"):
        # Add user message
        st.session_state.messages.append({"role": "user", "content": prompt})

        with st.chat_message("user"):
            st.markdown(prompt)

        # Process message
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                result = run_async(process_message(prompt, min_confidence, min_arousal, min_depth))

            st.markdown(result["response"])

            # Update user message metadata
            st.session_state.messages[-1]["metadata"] = {
                "emotion": result["emotion"],
                "need": result["need"]
            }

            # Show metadata
            if result.get("emotion"):
                st.caption(f"🎭 {result['emotion'].get('labels', '')} (valence: {result['emotion'].get('valence', 0):.2f}, arousal: {result['emotion'].get('arousal', 0):.2f})")
            if result.get("need"):
                st.caption(f"🎯 Need: {result['need'].get('primary_need', '')} ({result['need'].get('confidence', 0):.2f})")

            # Show intervention if present
            if result.get("intervention"):
                with st.expander("💡 Intervention Offered"):
                    st.markdown(result["intervention"])

            # Add assistant message
            st.session_state.messages.append({
                "role": "assistant",
                "content": result["response"],
                "intervention": result.get("intervention")
            })

        st.rerun()

with col2:
    st.subheader("📊 Emotional Trajectory")

    # Get latest emotion arc from last message
    if st.session_state.messages:
        # Process current state to get emotion arc
        try:
            with st.spinner("Updating chart..."):
                # Get emotion arc by making a dummy call
                orchestrator = boot_orchestrator()
                arc_result = run_async(orchestrator.call_tool("get_emotion_arc", {}))
                arc_content = arc_result.content[0].text if arc_result.content else "{}"
                emotion_arc = json.loads(arc_content)

                fig = create_emotion_plot(emotion_arc)
                st.plotly_chart(fig, use_container_width=True)
        except Exception as e:
            st.error(f"Could not load trajectory: {e}")
    else:
        fig = create_emotion_plot({})
        st.plotly_chart(fig, use_container_width=True)

# Footer
st.markdown("---")
st.markdown("""
### 🎯 Try These Examples

**Connection needs:** "I feel so isolated and alone"
**Autonomy needs:** "I feel so trapped and powerless"
**Security needs:** "Everything feels so uncertain"
**Rest needs:** "I'm so burnt out and exhausted"
**Recognition needs:** "Nobody notices all the work I do"

💡 *Interventions appear on the 2nd message when thresholds are met.*
""")