import gradio as gr

CUSTOM_CSS = """
body, .gradio-container {
    background: linear-gradient(180deg, #08101e 0%, #0b1020 100%);
    color: white !important;
    font-family: Inter, Arial, sans-serif;
}
.hero {
    padding: 24px;
    border: 1px solid rgba(255,255,255,0.08);
    border-radius: 20px;
    background: rgba(255,255,255,0.03);
    margin-bottom: 16px;
}
.metric {
    padding: 16px;
    border-radius: 16px;
    background: rgba(255,255,255,0.03);
    border: 1px solid rgba(255,255,255,0.08);
}
"""

def analyze_thesis(ticker, direction, horizon, position_size, thesis):
    thesis_lower = thesis.lower()

    thesis_score = 60
    evidence_score = 55
    risk_score = 45

    if "valuation" in thesis_lower:
        thesis_score += 8
        evidence_score += 8
    if "risk" in thesis_lower:
        thesis_score += 6
    if "earnings" in thesis_lower or "catalyst" in thesis_lower:
        evidence_score += 8
    if "guaranteed" in thesis_lower or "100%" in thesis_lower:
        risk_score += 20
        thesis_score -= 10

    if position_size > 20:
        risk_score += 15

    thesis_score = max(0, min(100, thesis_score))
    evidence_score = max(0, min(100, evidence_score))
    risk_score = max(0, min(100, risk_score))
    capital_readiness = max(0, min(100, int(thesis_score * 0.5 + evidence_score * 0.3 + (100 - risk_score) * 0.2)))

    contradictions = []
    if direction == "Bullish" and "overvalued" in thesis_lower:
        contradictions.append("Bullish view conflicts with 'overvalued' wording.")
    if direction == "Bearish" and "undervalued" in thesis_lower:
        contradictions.append("Bearish view conflicts with 'undervalued' wording.")
    if not contradictions:
        contradictions.append("No major contradiction detected.")

    missing = []
    if "valuation" not in thesis_lower:
        missing.append("Valuation context missing.")
    if "risk" not in thesis_lower:
        missing.append("Risk definition missing.")
    if "stop loss" not in thesis_lower and "invalidation" not in thesis_lower:
        missing.append("Exit or invalidation plan missing.")
    if "macro" not in thesis_lower:
        missing.append("Macro sensitivity not discussed.")

    counter_case = []
    if direction == "Bullish":
        counter_case = [
            "Positive expectations may already be priced in.",
            "Weak guidance can break the thesis quickly.",
            "Position sizing may be too aggressive for current evidence."
        ]
    else:
        counter_case = [
            "Negative sentiment may already be priced in.",
            "A strong earnings beat can invalidate the bearish view.",
            "Bear thesis may underestimate business resilience."
        ]

    memo = f"""
# BetaTwins Memo

**Ticker:** {ticker}  
**Direction:** {direction}  
**Horizon:** {horizon}  
**Position Size:** {position_size}%  

## Thesis
{thesis}

## Scores
- Thesis Score: {thesis_score}/100
- Evidence Score: {evidence_score}/100
- Risk Score: {risk_score}/100
- Capital Readiness: {capital_readiness}/100

## Contradictions
- """ + "\n- ".join(contradictions) + """

## Missing Factors
- """ + "\n- ".join(missing) + """

## Counter-Case
- """ + "\n- ".join(counter_case)

    summary = f"""
### Executive Summary
**{ticker}** thesis reviewed with a **{direction.lower()}** stance.

- Thesis Score: **{thesis_score}**
- Evidence Score: **{evidence_score}**
- Risk Score: **{risk_score}**
- Capital Readiness: **{capital_readiness}**
"""

    return (
        f"<div class='metric'><b>Thesis Score</b><br><span style='font-size:32px'>{thesis_score}</span></div>",
        f"<div class='metric'><b>Evidence Score</b><br><span style='font-size:32px'>{evidence_score}</span></div>",
        f"<div class='metric'><b>Risk Score</b><br><span style='font-size:32px'>{risk_score}</span></div>",
        f"<div class='metric'><b>Capital Readiness</b><br><span style='font-size:32px'>{capital_readiness}</span></div>",
        summary,
        "\n".join([f"- {x}" for x in contradictions]),
        "\n".join([f"- {x}" for x in missing]),
        "\n".join([f"- {x}" for x in counter_case]),
        memo
    )

with gr.Blocks(css=CUSTOM_CSS, title="BetaTwins AI") as demo:
    gr.HTML("""
    <div class="hero">
        <h1>Stress-test every investment thesis before capital is deployed.</h1>
        <p>BetaTwins helps traders, investors, and fintech teams detect weak reasoning, hidden assumptions, and missing risks before a decision becomes expensive.</p>
    </div>
    """)

    with gr.Tabs():
        with gr.Tab("Analyzer"):
            with gr.Row():
                with gr.Column(scale=4):
                    ticker = gr.Textbox(label="Ticker", placeholder="e.g. NVDA")
                    direction = gr.Dropdown(["Bullish", "Bearish", "Neutral"], value="Bullish", label="Direction")
                    horizon = gr.Dropdown(["Short-Term", "Swing", "Long-Term"], value="Swing", label="Time Horizon")
                    position_size = gr.Slider(1, 100, value=10, step=1, label="Position Size %")
                    thesis = gr.Textbox(label="Investment Thesis", lines=10, placeholder="Write your thesis here...")
                    run_btn = gr.Button("Run Analysis")
                with gr.Column(scale=6):
                    with gr.Row():
                        score1 = gr.HTML()
                        score2 = gr.HTML()
                        score3 = gr.HTML()
                        score4 = gr.HTML()
                    with gr.Tabs():
                        with gr.Tab("Executive Summary"):
                            summary = gr.Markdown()
                        with gr.Tab("Contradictions"):
                            contradictions = gr.Markdown()
                        with gr.Tab("Missing Factors"):
                            missing = gr.Markdown()
                        with gr.Tab("Counter-Case"):
                            counter = gr.Markdown()
                        with gr.Tab("Memo"):
                            memo = gr.Markdown()

            run_btn.click(
                fn=analyze_thesis,
                inputs=[ticker, direction, horizon, position_size, thesis],
                outputs=[score1, score2, score3, score4, summary, contradictions, missing, counter, memo]
            )

        with gr.Tab("About"):
            gr.Markdown("""
## BetaTwins AI
AI-powered thesis stress testing for smarter investment decisions.

### What it does
- Scores thesis quality
- Detects contradictions
- Finds missing factors
- Generates counter-case
- Builds memo-style output

### Disclaimer
This tool is for research and decision-support only. It is not financial advice.
""")

if __name__ == "__main__":
    demo.launch()