import gradio as gr
import json
import os
from datetime import datetime
from src.pipeline import run_pipeline, STOCKS, CONFIG
# ── CACHED RESULTS ────────────────────────────────────────────────────────────
cached_results = None
def load_cached_results():
"""Load last pipeline results if available."""
path = "artifacts/pipeline_results.json"
if os.path.exists(path):
with open(path) as f:
return json.load(f)
return None
def run_and_display(
selected_stocks: list,
strategy: str,
max_weight: float,
horizon_days: int
):
"""Run pipeline and return results for Gradio display."""
global cached_results
if not selected_stocks:
return "⚠️ Please select at least 3 stocks.", "", "", ""
config = {
"horizon_days": int(horizon_days),
"strategy": strategy,
"max_weight": max_weight / 100,
"min_weight": 0.02
}
try:
results = run_pipeline(
stocks=selected_stocks,
config=config,
save_results=True,
send_email=False # Don't send email from UI
)
cached_results = results
return format_outputs(results)
except Exception as e:
return f"❌ Pipeline failed: {e}", "", "", ""
def format_outputs(results):
"""Format pipeline results for display."""
# Portfolio table
portfolio = results.get("portfolio", {})
forecasts = results.get("forecasts", {})
sentiment = results.get("sentiment", {})
metrics = results.get("metrics", {})
explanation = results.get("explanation", "Not available")
# Portfolio allocation text
portfolio_text = "📊 PORTFOLIO ALLOCATION\n"
portfolio_text += "─" * 45 + "\n"
portfolio_text += f"{'Stock':<8} {'Weight':>8} {'Forecast':>10} {'Sentiment':>12}\n"
portfolio_text += "─" * 45 + "\n"
for stock, weight in sorted(portfolio.items(),
key=lambda x: x[1], reverse=True):
ret = forecasts.get(stock, {}).get("return", 0)
sig = sentiment.get(stock, {}).get("signal", "neutral")
icon = "🟢" if sig == "bullish" else "🔴" if sig == "bearish" else "⚪"
bar = "█" * int(weight * 40)
portfolio_text += (f"{stock:<8} {weight:>7.1%} {ret:>+10.2%} "
f"{icon} {sig:<10}\n")
portfolio_text += f" {bar}\n"
# Metrics text
metrics_text = "📈 PORTFOLIO METRICS\n"
metrics_text += "─" * 35 + "\n"
metrics_text += f"Expected Return: {metrics.get('expected_return', 0):>+8.2%}\n"
metrics_text += f"Volatility: {metrics.get('volatility', 0):>8.2%}\n"
metrics_text += f"Sharpe Ratio: {metrics.get('sharpe_ratio', 0):>8.2f}\n"
metrics_text += f"Max Drawdown: {metrics.get('max_drawdown', 0):>8.2%}\n"
metrics_text += f"Beta: {metrics.get('beta', 0):>8.2f}\n"
# Sentiment text
sentiment_text = "🤖 SENTIMENT SIGNALS\n"
sentiment_text += "─" * 35 + "\n"
for stock, sent in sentiment.items():
score = sent.get("sentiment_score", 0)
sig = sent.get("signal", "neutral")
conf = sent.get("confidence", 0)
icon = "🟢" if sig == "bullish" else "🔴" if sig == "bearish" else "⚪"
sentiment_text += (f"{stock:<6} {icon} {sig:<8} "
f"score={score:+.2f} conf={conf:.0%}\n")
return portfolio_text, metrics_text, sentiment_text, explanation
custom_css = """
:root {
--primary: #0D9488;
--primary-light: #5EEAD4;
--border: #ccfbf1;
--surface: #f0fdfa;
--radius: 16px;
--shadow: 0 4px 24px rgba(13,148,136,0.10);
}
body, .gradio-container {
background: linear-gradient(135deg, #f0fdfa, #e6fffa) !important;
font-family: 'Inter', sans-serif !important;
}
.app-header {
background: linear-gradient(135deg, #0D9488, #14B8A6);
border-radius: var(--radius);
padding: 2rem;
text-align: center;
color: white;
margin-bottom: 1.5rem;
}
.card {
background: white !important;
border-radius: var(--radius) !important;
border: 1px solid var(--border) !important;
padding: 1rem !important;
box-shadow: var(--shadow) !important;
}
button.primary {
background: linear-gradient(135deg, #0D9488, #14B8A6) !important;
border: none !important;
color: white !important;
font-weight: 600 !important;
}
"""
ALL_STOCKS = [
"AAPL", "MSFT", "GOOGL", "TSLA", "NVDA",
"META", "AMZN", "NFLX", "NOW", "AMD",
"INTC", "CRM", "ORCL", "ADBE", "PYPL"
]
def create_app():
with gr.Blocks(title="Portfolio Optimizer", css=custom_css) as app:
gr.HTML("""
""")
with gr.Row():
# ── LEFT PANEL — Controls ──────────────────────────────────────
with gr.Column(scale=1, elem_classes="card"):
gr.HTML("⚙️ Configuration
")
selected_stocks = gr.CheckboxGroup(
label="Select Stocks",
choices=ALL_STOCKS,
value=["AAPL", "MSFT", "GOOGL", "TSLA", "NVDA"],
)
strategy = gr.Dropdown(
label="Optimization Strategy",
choices=[
"max_sharpe",
"min_variance",
"max_return",
"risk_parity"
],
value="max_sharpe"
)
max_weight = gr.Slider(
label="Max Weight per Stock (%)",
minimum=10,
maximum=60,
value=40,
step=5
)
horizon = gr.Slider(
label="Forecast Horizon (days)",
minimum=7,
maximum=90,
value=30,
step=7
)
run_btn = gr.Button(
"🚀 Run Portfolio Optimization",
variant="primary"
)
gr.HTML("""
⏱️ Takes ~30-60 seconds to complete
🤖 Uses Groq LLaMA 3 for sentiment analysis
📊 Gradient Boosting for return forecasting
""")
# ── RIGHT PANEL — Results ──────────────────────────────────────
with gr.Column(scale=2):
with gr.Tabs():
with gr.Tab("📊 Portfolio"):
portfolio_out = gr.Textbox(
label="Portfolio Allocation",
lines=20,
interactive=False,
elem_classes="card"
)
with gr.Tab("📈 Metrics"):
metrics_out = gr.Textbox(
label="Performance Metrics",
lines=10,
interactive=False,
elem_classes="card"
)
with gr.Tab("🤖 Sentiment"):
sentiment_out = gr.Textbox(
label="GenAI Sentiment Signals",
lines=12,
interactive=False,
elem_classes="card"
)
with gr.Tab("💬 AI Analysis"):
explanation_out = gr.Textbox(
label="LLM Portfolio Explanation",
lines=12,
interactive=False,
elem_classes="card"
)
run_btn.click(
fn=run_and_display,
inputs=[selected_stocks, strategy, max_weight, horizon],
outputs=[portfolio_out, metrics_out, sentiment_out, explanation_out]
)
gr.HTML("""
⚠️ For informational purposes only. Not financial advice.
Built with LangChain · LangGraph · Groq · Gradio
""")
return app
if __name__ == "__main__":
app = create_app()
app.launch(server_name="0.0.0.0", server_port=7860, share=False)