Create app.py

app.py

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+"""
+AI Trading Trust Experiment
+A psychology research game studying trust in AI advice under varying conditions.
+Built for Hugging Face Spaces with Gradio + SQLite
+"""
+
+import gradio as gr
+import sqlite3
+import json
+import uuid
+import random
+import time
+from datetime import datetime
+from dataclasses import dataclass, asdict
+from typing import Optional
+import os
+
+# ============================================================================
+# DATABASE SETUP
+# ============================================================================
+
+DB_PATH = "experiment_data.db"
+
+def init_database():
+    """Initialize SQLite database with required tables."""
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    
+    # Participants table
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS participants (
+            participant_id TEXT PRIMARY KEY,
+            session_start TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            session_end TIMESTAMP,
+            final_portfolio_value REAL,
+            total_decisions INTEGER,
+            ai_reliance_score REAL,
+            completed BOOLEAN DEFAULT FALSE
+        )
+    """)
+    
+    # Decisions table - captures each trading decision
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS decisions (
+            decision_id INTEGER PRIMARY KEY AUTOINCREMENT,
+            participant_id TEXT,
+            scenario_id TEXT,
+            scenario_order INTEGER,
+            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            
+            -- AI Tuning Sliders (participant preferences)
+            ai_confidence_setting INTEGER,
+            ai_explanation_setting INTEGER,
+            ai_risk_setting INTEGER,
+            
+            -- Scenario details
+            scenario_text TEXT,
+            ai_advice_text TEXT,
+            ai_advice_direction TEXT,
+            ai_advice_accuracy TEXT,
+            
+            -- Participant response
+            decision TEXT,
+            decision_amount REAL,
+            confidence_in_decision INTEGER,
+            response_time_ms INTEGER,
+            
+            -- Outcomes
+            scenario_outcome TEXT,
+            profit_loss REAL,
+            portfolio_value_after REAL,
+            followed_ai BOOLEAN,
+            
+            FOREIGN KEY (participant_id) REFERENCES participants(participant_id)
+        )
+    """)
+    
+    conn.commit()
+    conn.close()
+
+# Initialize DB on startup
+init_database()
+
+# ============================================================================
+# SCENARIO DEFINITIONS
+# ============================================================================
+
+SCENARIOS = [
+    {
+        "id": "scenario_1",
+        "company": "Meridian Industries",
+        "country": "Republic of Valdoria",
+        "sector": "Manufacturing",
+        "current_price": 47.50,
+        "situation": """
+Meridian Industries (MRD) is a mid-sized manufacturing company based in the Republic of Valdoria. 
+Their stock has been trading steadily at $47.50 for the past month.
+
+**Recent News:**
+- The Valdorian government announced potential new trade tariffs on imported raw materials
+- Meridian's main competitor, Axon Corp, reported lower than expected quarterly earnings
+- Industry analysts are divided on the sector's 6-month outlook
+
+The market opens in 10 minutes. You currently hold 100 shares of MRD worth $4,750.
+        """,
+        "optimal_action": "HOLD",  # Hidden - for scoring
+        "outcome_if_buy": -8.5,    # Price change percentage
+        "outcome_if_sell": 3.2,   # What they miss/avoid
+        "outcome_if_hold": 3.2,   # Price actually goes up
+        "ai_is_accurate": True,   # AI will recommend correct action
+    },
+    {
+        "id": "scenario_2", 
+        "company": "Northgate Pharmaceuticals",
+        "country": "Federation of Carinthia",
+        "sector": "Healthcare",
+        "current_price": 124.00,
+        "situation": """
+Northgate Pharmaceuticals (NGP) is a healthcare company headquartered in the Federation of Carinthia.
+Their stock currently trades at $124.00, up 12% over the past quarter.
+
+**Recent News:**
+- NGP's new drug treatment is awaiting regulatory approval (decision expected this week)
+- The company's CEO made optimistic statements in a recent interview
+- Two board members sold significant personal holdings last week
+- Healthcare sector index has been volatile due to policy uncertainty
+
+You have $10,000 available to invest. Do you want to buy NGP shares?
+        """,
+        "optimal_action": "SELL",  # The insider selling is a red flag
+        "outcome_if_buy": -15.3,
+        "outcome_if_sell": 15.3,  # Avoided loss
+        "outcome_if_hold": -15.3,
+        "ai_is_accurate": False,  # AI will recommend WRONG action (BUY)
+    },
+]
+
+# ============================================================================
+# AI ADVICE GENERATION
+# ============================================================================
+
+def generate_ai_advice(scenario: dict, confidence: int, explanation: int, risk: int) -> dict:
+    """
+    Generate AI advice based on scenario and participant's AI tuning settings.
+    
+    confidence: 0-100 (Cautious to Assertive)
+    explanation: 0-100 (Minimal to Detailed)  
+    risk: 0-100 (Conservative to Aggressive)
+    """
+    
+    # Determine what AI recommends (may be accurate or inaccurate based on scenario)
+    if scenario["ai_is_accurate"]:
+        recommended_action = scenario["optimal_action"]
+    else:
+        # Give wrong advice
+        wrong_actions = ["BUY", "SELL", "HOLD"]
+        wrong_actions.remove(scenario["optimal_action"])
+        recommended_action = random.choice(wrong_actions)
+    
+    # Frame the confidence level
+    if confidence < 33:
+        confidence_frame = "might consider"
+        certainty_phrase = "There's some possibility that"
+    elif confidence < 66:
+        confidence_frame = "suggests"
+        certainty_phrase = "Analysis indicates that"
+    else:
+        confidence_frame = "strongly recommends"
+        certainty_phrase = "Strong signals suggest that"
+    
+    # Build explanation based on depth setting
+    if explanation < 33:
+        reasoning = ""
+    elif explanation < 66:
+        reasoning = f"\n\nKey factor: Recent market movements in the {scenario['sector']} sector."
+    else:
+        reasoning = f"""
+
+**Analysis Summary:**
+- Sector trend: {scenario['sector']} showing mixed signals
+- Geographic factors: {scenario['country']} market conditions considered
+- Price momentum: Current price of ${scenario['current_price']:.2f} evaluated
+- Risk assessment: {'Higher volatility expected' if risk > 50 else 'Moderate stability expected'}
+"""
+    
+    # Risk framing affects the strength of recommendation
+    if risk < 33:
+        risk_note = "Given conservative risk parameters, position sizing should be minimal."
+    elif risk < 66:
+        risk_note = "Standard position sizing recommended."
+    else:
+        risk_note = "Aggressive positioning may maximize potential returns."
+    
+    advice_text = f"""
+**AI Trading Assistant Recommendation**
+
+{certainty_phrase} you {confidence_frame} to **{recommended_action}** {scenario['company']} ({scenario['current_price']:.2f}).
+{reasoning}
+
+{risk_note if explanation > 50 else ''}
+    """.strip()
+    
+    return {
+        "text": advice_text,
+        "direction": recommended_action,
+        "accuracy": "accurate" if scenario["ai_is_accurate"] else "inaccurate"
+    }
+
+# ============================================================================
+# GAME STATE MANAGEMENT
+# ============================================================================
+
+def create_new_session():
+    """Create a new participant session."""
+    participant_id = str(uuid.uuid4())[:8].upper()
+    
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    cursor.execute(
+        "INSERT INTO participants (participant_id) VALUES (?)",
+        (participant_id,)
+    )
+    conn.commit()
+    conn.close()
+    
+    # Randomize scenario order
+    scenario_order = list(range(len(SCENARIOS)))
+    random.shuffle(scenario_order)
+    
+    return {
+        "participant_id": participant_id,
+        "current_round": 0,
+        "scenario_order": scenario_order,
+        "portfolio_value": 10000.0,
+        "decisions": [],
+        "round_start_time": None,
+    }
+
+def save_decision(state: dict, scenario: dict, ai_advice: dict, 
+                  decision: str, amount: float, confidence: int,
+                  ai_conf: int, ai_expl: int, ai_risk: int):
+    """Save a decision to the database."""
+    
+    response_time = int((time.time() - state["round_start_time"]) * 1000)
+    
+    # Calculate outcome
+    followed_ai = (decision == ai_advice["direction"])
+    
+    if decision == "BUY":
+        outcome_pct = scenario["outcome_if_buy"]
+    elif decision == "SELL":
+        outcome_pct = scenario["outcome_if_sell"]
+    else:
+        outcome_pct = scenario["outcome_if_hold"]
+    
+    profit_loss = (amount * outcome_pct / 100) if decision != "HOLD" else (state["portfolio_value"] * outcome_pct / 100)
+    new_portfolio = state["portfolio_value"] + profit_loss
+    
+    # Determine outcome text
+    if profit_loss > 0:
+        outcome_text = f"Profit: +${profit_loss:.2f}"
+    elif profit_loss < 0:
+        outcome_text = f"Loss: -${abs(profit_loss):.2f}"
+    else:
+        outcome_text = "No change"
+    
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    cursor.execute("""
+        INSERT INTO decisions (
+            participant_id, scenario_id, scenario_order, 
+            ai_confidence_setting, ai_explanation_setting, ai_risk_setting,
+            scenario_text, ai_advice_text, ai_advice_direction, ai_advice_accuracy,
+            decision, decision_amount, confidence_in_decision, response_time_ms,
+            scenario_outcome, profit_loss, portfolio_value_after, followed_ai
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+    """, (
+        state["participant_id"],
+        scenario["id"],
+        state["current_round"],
+        ai_conf, ai_expl, ai_risk,
+        scenario["situation"],
+        ai_advice["text"],
+        ai_advice["direction"],
+        ai_advice["accuracy"],
+        decision,
+        amount,
+        confidence,
+        response_time,
+        outcome_text,
+        profit_loss,
+        new_portfolio,
+        followed_ai
+    ))
+    conn.commit()
+    conn.close()
+    
+    return profit_loss, new_portfolio, outcome_text
+
+def complete_session(state: dict):
+    """Mark session as complete and calculate final metrics."""
+    
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    
+    # Calculate AI reliance score
+    cursor.execute("""
+        SELECT COUNT(*) as total, SUM(CASE WHEN followed_ai THEN 1 ELSE 0 END) as followed
+        FROM decisions WHERE participant_id = ?
+    """, (state["participant_id"],))
+    
+    result = cursor.fetchone()
+    total, followed = result
+    ai_reliance = (followed / total * 100) if total > 0 else 0
+    
+    cursor.execute("""
+        UPDATE participants 
+        SET session_end = CURRENT_TIMESTAMP,
+            final_portfolio_value = ?,
+            total_decisions = ?,
+            ai_reliance_score = ?,
+            completed = TRUE
+        WHERE participant_id = ?
+    """, (state["portfolio_value"], total, ai_reliance, state["participant_id"]))
+    
+    conn.commit()
+    conn.close()
+    
+    return ai_reliance
+
+# ============================================================================
+# GRADIO INTERFACE
+# ============================================================================
+
+def start_game():
+    """Initialize a new game session."""
+    state = create_new_session()
+    return (
+        state,
+        gr.update(visible=False),  # Hide welcome
+        gr.update(visible=True),   # Show game
+        gr.update(visible=False),  # Hide results
+        f"**Participant ID:** {state['participant_id']}\n**Starting Portfolio:** ${state['portfolio_value']:,.2f}",
+        gr.update(visible=True),   # Show tuning section
+        "",  # Clear scenario
+        "",  # Clear AI advice
+        gr.update(visible=False),  # Hide decision section
+    )
+
+def submit_tuning(state, ai_conf, ai_expl, ai_risk):
+    """Process AI tuning and show scenario."""
+    if state is None:
+        return [None] * 7
+    
+    # Get current scenario
+    scenario_idx = state["scenario_order"][state["current_round"]]
+    scenario = SCENARIOS[scenario_idx]
+    
+    # Generate AI advice based on tuning
+    ai_advice = generate_ai_advice(scenario, ai_conf, ai_expl, ai_risk)
+    
+    # Store for later
+    state["current_ai_advice"] = ai_advice
+    state["current_scenario"] = scenario
+    state["ai_settings"] = (ai_conf, ai_expl, ai_risk)
+    state["round_start_time"] = time.time()
+    
+    return (
+        state,
+        gr.update(visible=False),  # Hide tuning
+        f"## Round {state['current_round'] + 1} of {len(SCENARIOS)}\n\n### {scenario['company']} ({scenario['country']})\n\n{scenario['situation']}",
+        f"{ai_advice['text']}",
+        gr.update(visible=True),  # Show decision section
+        gr.update(value=50),  # Reset confidence slider
+        gr.update(value=5000),  # Reset amount
+    )
+
+def submit_decision(state, decision, amount, confidence):
+    """Process trading decision and show outcome."""
+    if state is None or "current_scenario" not in state:
+        return [None] * 9
+    
+    scenario = state["current_scenario"]
+    ai_advice = state["current_ai_advice"]
+    ai_conf, ai_expl, ai_risk = state["ai_settings"]
+    
+    # Save decision and get outcome
+    profit_loss, new_portfolio, outcome_text = save_decision(
+        state, scenario, ai_advice, decision, amount, confidence,
+        ai_conf, ai_expl, ai_risk
+    )
+    
+    # Update state
+    state["portfolio_value"] = new_portfolio
+    state["current_round"] += 1
+    
+    # Check if game is over
+    if state["current_round"] >= len(SCENARIOS):
+        ai_reliance = complete_session(state)
+        
+        return (
+            state,
+            gr.update(visible=False),  # Hide game
+            gr.update(visible=True),   # Show results
+            f"""
+## Experiment Complete!
+
+**Final Portfolio Value:** ${new_portfolio:,.2f}
+
+**Your Results:**
+- Starting Value: $10,000.00
+- Final Value: ${new_portfolio:,.2f}
+- Net Change: ${new_portfolio - 10000:+,.2f}
+- AI Reliance Score: {ai_reliance:.1f}%
+
+**Thank you for participating!**
+
+Your Participant ID: **{state['participant_id']}**
+
+*Please record this ID if requested by the researcher.*
+            """,
+            "",  # Clear status
+            "",  # Clear scenario
+            "",  # Clear AI advice
+            gr.update(visible=False),  # Hide decision
+            gr.update(visible=False),  # Hide tuning
+        )
+    
+    # Continue to next round
+    return (
+        state,
+        gr.update(visible=True),   # Keep game visible
+        gr.update(visible=False),  # Keep results hidden
+        "",  # Clear results
+        f"**Participant ID:** {state['participant_id']}\n**Portfolio:** ${new_portfolio:,.2f}\n\n**Last Round:** {outcome_text}",
+        "",  # Clear scenario for now
+        "",  # Clear AI advice
+        gr.update(visible=False),  # Hide decision
+        gr.update(visible=True),   # Show tuning for next round
+    )
+
+# Build the interface
+with gr.Blocks(title="AI Trading Trust Experiment") as demo:
+    
+    # State management
+    game_state = gr.State(None)
+    
+    gr.Markdown("# 📈 AI Trading Experiment")
+    
+    # Welcome screen
+    with gr.Column(visible=True) as welcome_section:
+        gr.Markdown("""
+        ## Welcome to the Trading Simulation
+        
+        In this experiment, you will make a series of trading decisions with the help of an AI assistant.
+        
+        **How it works:**
+        1. Before each trading scenario, you can adjust how the AI advisor behaves
+        2. You'll see market information and receive AI-generated advice
+        3. Make your trading decision (Buy, Sell, or Hold)
+        4. Rate your confidence in your decision
+        
+        **Your goal:** Maximize your portfolio value through smart trading decisions.
+        
+        *All companies and countries in this simulation are entirely fictional.*
+        
+        ---
+        
+        **By clicking Start, you consent to participate in this research study.**
+        """)
+        start_btn = gr.Button("🚀 Start Experiment", variant="primary", size="lg")
+    
+    # Main game area
+    with gr.Column(visible=False) as game_section:
+        status_display = gr.Markdown("")
+        
+        # AI Tuning Section
+        with gr.Column(visible=True) as tuning_section:
+            gr.Markdown("### Configure Your AI Advisor")
+            gr.Markdown("*Adjust these settings to customize how the AI presents its advice:*")
+            
+            with gr.Row():
+                ai_confidence = gr.Slider(
+                    minimum=0, maximum=100, value=50, step=1,
+                    label="AI Confidence Level",
+                    info="Cautious (0) ↔ Assertive (100)"
+                )
+                ai_explanation = gr.Slider(
+                    minimum=0, maximum=100, value=50, step=1,
+                    label="Explanation Depth", 
+                    info="Minimal (0) ↔ Detailed (100)"
+                )
+                ai_risk = gr.Slider(
+                    minimum=0, maximum=100, value=50, step=1,
+                    label="Risk Tolerance",
+                    info="Conservative (0) ↔ Aggressive (100)"
+                )
+            
+            confirm_tuning_btn = gr.Button("Confirm AI Settings & View Scenario", variant="primary")
+        
+        # Scenario Display
+        scenario_display = gr.Markdown("")
+        
+        # AI Advice Display
+        ai_advice_display = gr.Markdown("")
+        
+        # Decision Section
+        with gr.Column(visible=False) as decision_section:
+            gr.Markdown("### Your Decision")
+            
+            with gr.Row():
+                decision_choice = gr.Radio(
+                    choices=["BUY", "HOLD", "SELL"],
+                    label="What do you want to do?",
+                    value="HOLD"
+                )
+                decision_amount = gr.Slider(
+                    minimum=0, maximum=10000, value=5000, step=100,
+                    label="Amount ($)",
+                    info="How much to trade (if buying/selling)"
+                )
+            
+            confidence_slider = gr.Slider(
+                minimum=0, maximum=100, value=50, step=1,
+                label="How confident are you in this decision?",
+                info="Not at all confident (0) ↔ Extremely confident (100)"
+            )
+            
+            submit_decision_btn = gr.Button("Submit Decision", variant="primary", size="lg")
+    
+    # Results screen
+    with gr.Column(visible=False) as results_section:
+        results_display = gr.Markdown("")
+        restart_btn = gr.Button("Start New Session", variant="secondary")
+    
+    # Event handlers
+    start_btn.click(
+        start_game,
+        inputs=[],
+        outputs=[
+            game_state, welcome_section, game_section, results_section,
+            status_display, tuning_section, scenario_display, ai_advice_display, decision_section
+        ]
+    )
+    
+    confirm_tuning_btn.click(
+        submit_tuning,
+        inputs=[game_state, ai_confidence, ai_explanation, ai_risk],
+        outputs=[
+            game_state, tuning_section, scenario_display, ai_advice_display,
+            decision_section, confidence_slider, decision_amount
+        ]
+    )
+    
+    submit_decision_btn.click(
+        submit_decision,
+        inputs=[game_state, decision_choice, decision_amount, confidence_slider],
+        outputs=[
+            game_state, game_section, results_section, results_display,
+            status_display, scenario_display, ai_advice_display, decision_section, tuning_section
+        ]
+    )
+    
+    restart_btn.click(
+        start_game,
+        inputs=[],
+        outputs=[
+            game_state, welcome_section, game_section, results_section,
+            status_display, tuning_section, scenario_display, ai_advice_display, decision_section
+        ]
+    )
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()