app.py

import gradio as gr
from services.llm_client import llm_service
from core.analyzer import PortfolioAnalyzer
from core.comparer import PortfolioComparer
from core.chat import ChatAssistant
from core.metrics import show_metrics_table
from core.visualization import build_alpha_chart
from core.crypto_dashboard import build_crypto_dashboard  # 🆕 новая вкладка

# === Model setup ===
MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"

analyzer = PortfolioAnalyzer(llm_service, MODEL_NAME)
comparer = PortfolioComparer(llm_service, MODEL_NAME)
chatbot = ChatAssistant(llm_service, MODEL_NAME)

# === Theme ===
dark_theme = gr.themes.Base(
    primary_hue="violet",
    secondary_hue="gray",
    neutral_hue="zinc",
).set(
    body_background_fill="#0d1117",
    body_text_color="#e6edf3",
    block_background_fill="#161b22",
    block_border_color="#30363d",
    button_primary_background_fill="#4f46e5",
    button_primary_background_fill_hover="#6366f1",
    button_primary_text_color="#ffffff",
    input_background_fill="#0d1117",
    block_shadow="0 2px 6px rgba(0,0,0,0.3)",
    block_label_text_color="#9da5b4",
    block_label_text_size="sm",
)

with gr.Blocks(theme=dark_theme, css="""
#root, [data-testid="block-container"] {
    max-width: 1050px !important;
    margin: auto !important;
}
.gradio-container { font-family: 'Inter', sans-serif; background-color:#0d1117!important;}
#llm_comment_box textarea,#analysis_output textarea{
    min-height:520px!important;background-color:#161b22!important;color:#f0f6fc!important;
    border:1px solid #30363d!important;border-radius:6px!important;font-family:'JetBrains Mono',monospace!important;
}
.gr-dataframe table{width:100%!important;color:#c9d1d9!important;background:#161b22!important;}
.gr-dataframe th{background:#21262d!important;color:#f0f6fc!important;border-bottom:1px solid #30363d!important;}
.gr-dataframe td{border-top:1px solid #30363d!important;padding:8px!important;}
""") as demo:

    gr.Markdown("## Investment Portfolio Analyzer")
    gr.Markdown(
        "Professional AI-driven analytics for investment and crypto markets.",
        elem_classes="subtitle",
    )

    with gr.Tabs():
        # --- Analysis ---
        with gr.TabItem("Analysis"):
            portfolio_input = gr.Textbox(label="Portfolio ID or Link",
                                         placeholder="Enter portfolio ID (uuid)",
                                         value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
            analyze_btn = gr.Button("Run Analysis", variant="primary")
            analyze_out = gr.Textbox(label="Analysis Result", lines=15, elem_id="analysis_output")
            analyze_btn.click(fn=analyzer.run, inputs=portfolio_input, outputs=analyze_out)

        # --- Comparison ---
        with gr.TabItem("Comparison Table"):
            from core.comparison_table import show_comparison_table
            pid_a = gr.Textbox(label="Portfolio A", value="3852a354-e66e-4bc5-97e9-55124e31e687")
            pid_b = gr.Textbox(label="Portfolio B", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
            compare_btn = gr.Button("Load Comparison", variant="primary")
            comp_table = gr.Dataframe(label="Comparative Metrics", wrap=True)
            comp_comment = gr.Textbox(label="AI Commentary", lines=14, elem_id="llm_comment_box")
            compare_btn.click(fn=show_comparison_table,
                              inputs=[pid_a, pid_b],
                              outputs=[comp_table, comp_comment])

        # --- Assistant ---
        with gr.TabItem("Assistant"):
            chat_in = gr.Textbox(label="Ask about investments or analysis")
            chat_btn = gr.Button("Send Question", variant="primary")
            chat_out = gr.Textbox(label="AI Response", lines=8)
            chat_btn.click(fn=chatbot.run, inputs=chat_in, outputs=chat_out)

        # --- Metrics ---
        with gr.TabItem("Metrics Table"):
            metrics_in = gr.Textbox(label="Portfolio ID", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
            metrics_btn = gr.Button("Load Metrics", variant="primary")
            metrics_out = gr.Dataframe(label="Portfolio Metrics", wrap=True)
            metrics_btn.click(fn=show_metrics_table, inputs=metrics_in, outputs=metrics_out)

        # --- AlphaBTC Chart ---
        with gr.TabItem("AlphaBTC Chart"):
            chart_in = gr.Textbox(label="Portfolio ID", value="3852a354-e66e-4bc5-97e9-55124e31e687")
            chart_btn = gr.Button("Generate Chart", variant="primary")
            chart_out = gr.Plot(label="Alpha vs BTC")
            chart_btn.click(fn=build_alpha_chart, inputs=chart_in, outputs=chart_out)

        # --- Crypto Intelligence Dashboard (refactored) ---
        with gr.TabItem("Crypto Intelligence Dashboard"):
            gr.Markdown("### 📊 Unified Market Intelligence (Binance + TradingView)")

            with gr.Row():
                assets_select = gr.CheckboxGroup(
                    label="Select assets",
                    choices=["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT", "DOGEUSDT"],
                    value=["BTCUSDT", "ETHUSDT"]
                )
                start_picker = gr.DatePicker(label="Start Date", value=(datetime.now() - timedelta(days=365*2)))
                end_picker = gr.DatePicker(label="End Date", value=datetime.now())
                load_btn = gr.Button("Load Market Data", variant="primary")

            price_plot = gr.Plot(label="Recent Market (Binance)")
            hist_plot = gr.Plot(label="Historical Chart (TradingView)")
            ai_summary = gr.Textbox(label="AI Market Summary", lines=8, elem_id="llm_comment_box")

            from core.crypto_dashboard import build_crypto_dashboard
            load_btn.click(
                fn=build_crypto_dashboard,
                inputs=[assets_select, start_picker, end_picker],
                outputs=[price_plot, hist_plot, ai_summary],
                show_progress="minimal",
            )

    gr.Markdown("---")
    gr.Markdown(
        "<center><small style='color:#6e7681;'>Developed with Featherless.ai • Powered by OpenAI-compatible API</small></center>",
        elem_classes="footer",
    )

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