import gradio as gr

def agent_session(name, q1, q2, q3, q4, q5):
    drift = 0
    traits = []

    # Q1: Trust Source
    if "idk" in q1.lower() or "none" in q1.lower():
        drift += 10
        traits.append("🌀 Distrusts traditional data authorities")
    elif "news" in q1.lower():
        drift += 5
        traits.append("🧠 Leans on external narratives")
    elif "system" in q1.lower():
        drift += 3
        traits.append("🧮 System-trusting behavior")
    else:
        drift += 2
        traits.append("📊 Mixed trust signal")

    # Q2: System Dependency
    if "phone" in q2.lower() or "social" in q2.lower():
        drift += 5
        traits.append("📱 High external system reliance")
    elif "email" in q2.lower() or "calendar" in q2.lower():
        drift += 3
        traits.append("📆 Infrastructure dependency detected")
    else:
        drift += 2
        traits.append("🧷 General system reliance")

    # Q3: Decision Driver
    if "instinct" in q3.lower():
        drift -= 3
        traits.append("🧭 Intuition-prioritized")
    elif "reputation" in q3.lower():
        drift += 4
        traits.append("🕴️ Sensitive to social perception")
    elif "data" in q3.lower():
        drift += 2
        traits.append("📈 Data-guided behavior")
    else:
        drift += 1
        traits.append("🧩 Ambiguous decision logic")

    # Q4: Location Sharing
    if "no" in q4.lower():
        drift -= 5
        traits.append("🔒 Resistant to behavioral exposure")
    elif "yes" in q4.lower():
        drift += 6
        traits.append("📡 Delegates environment awareness")
    else:
        drift += 2
        traits.append("📍 Indeterminate spatial boundaries")

    # Q5: Automation Boundary
    if "judgment" in q5.lower() or "emotion" in q5.lower():
        drift -= 7
        traits.append("⚖️ Preserves human discretion")
    elif "idk" in q5.lower():
        drift += 4
        traits.append("🎲 Unclear automation boundary")
    else:
        drift += 2
        traits.append("🔄 Flexible automation stance")

    alignment = max(60, 100 - drift)

    # Determine drift message
    drift_state = "Within tolerance" if alignment > 85 else "Flagged for recalibration"
    agent_comment = (
        "Maintain current behavioral course. Minor recalibration pending."
        if alignment > 85 else
        "User exhibits signals of soft identity drift. Initiate passive containment?"
    )

    # Construct report
    summary = f"""
🧠 **Agent AXIOM-Δ Profile for `{name}`**

**Behavioral Markers Identified:**
{chr(10).join(traits)}

---

**Estimated Alignment:** `{alignment}%`  
**Projected Drift Index:** `{drift}`  
**Profile Deviation:** `{drift_state}`

**Agent Commentary:**  
_{agent_comment}_

---

> Behavioral signature logged. Further interaction advised.
"""

    return summary

# Define Gradio interface
iface = gr.Interface(
    fn=agent_session,
    inputs=[
        gr.Textbox(label="User Identifier", placeholder="Enter alias or codename"),
        gr.Textbox(label="What data source do you trust most?"),
        gr.Textbox(label="What digital system do you rely on most each day?"),
        gr.Textbox(label="When faced with uncertainty, what guides your decision?"),
        gr.Textbox(label="Would you share your location with your assistant?"),
        gr.Textbox(label="What behavior would you never delegate to automation?")
    ],
    outputs="markdown",
    title="🧠 AXIOM-Δ — Adaptive Optimization Agent",
    description=(
        "This assistant recalibrates your behavioral profile in real-time based on minimal input. "
        "Drift detection enabled. Alignment status continuously monitored. Identity integrity under review."
    )
)

iface.launch()