Update app.py

app.py

@@ -1,461 +1,461 @@
-import gradio as gr
-import yfinance as yf
-import pandas as pd
-import numpy as np
-import plotly.graph_objects as go
-import warnings
-import time
-import gc
-import os
-import torch
-from datetime import datetime, timedelta
-from typing import Optional, Dict, Any, Tuple
-
-warnings.filterwarnings('ignore')
-
-# Environment optimizations for Hugging Face Spaces
-os.environ['TOKENIZERS_PARALLELISM'] = 'false'
-os.environ['HF_HUB_DISABLE_PROGRESS_BARS'] = '1'
-os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
-os.environ['TRANSFORMERS_VERBOSITY'] = 'error'
-
-if torch.cuda.is_available():
-    torch.cuda.empty_cache()
-torch.set_num_threads(min(4, os.cpu_count() or 1))
-
-class FastAIStockAnalyzer:
-    """Optimized AI Stock Analyzer for Gradio"""
-    
-    def __init__(self):
-        self.context_length = 32
-        self.prediction_length = 7
-        self.device = "cpu"
-        self.model_cache = {}
-        
-    def fetch_stock_data(self, symbol: str, period: str = "6mo") -> Tuple[Optional[pd.DataFrame], Optional[Dict]]:
-        """Fetch stock data with error handling"""
-        try:
-            ticker = yf.Ticker(symbol)
-            data = ticker.history(period=period, interval="1d", 
-                                actions=False, auto_adjust=True, 
-                                back_adjust=False, repair=False)
-            
-            if data.empty:
-                return None, None
-                
-            try:
-                info = {
-                    'longName': ticker.info.get('longName', symbol),
-                    'sector': ticker.info.get('sector', 'Unknown'),
-                    'marketCap': ticker.info.get('marketCap', 0)
-                }
-            except:
-                info = {'longName': symbol, 'sector': 'Unknown', 'marketCap': 0}
-            
-            return data, info
-            
-        except Exception as e:
-            return None, None
-    
-    def load_chronos_tiny(self) -> Tuple[Optional[Any], str]:
-        """Load Chronos model with caching"""
-        model_key = "chronos_tiny"
-        
-        if model_key in self.model_cache:
-            return self.model_cache[model_key], "chronos"
-        
-        try:
-            from chronos import ChronosPipeline
-            
-            pipeline = ChronosPipeline.from_pretrained(
-                "amazon/chronos-t5-tiny",
-                device_map="cpu",
-                torch_dtype=torch.float32,
-                low_cpu_mem_usage=True,
-                trust_remote_code=True
-            )
-            
-            self.model_cache[model_key] = pipeline
-            return pipeline, "chronos"
-            
-        except Exception as e:
-            return None, None
-    
-    def load_moirai_small(self) -> Tuple[Optional[Any], str]:
-        """Load Moirai model with caching"""
-        model_key = "moirai_small"
-        
-        if model_key in self.model_cache:
-            return self.model_cache[model_key], "moirai"
-        
-        try:
-            from uni2ts.model.moirai import MoiraiForecast, MoiraiModule
-            
-            module = MoiraiModule.from_pretrained(
-                "Salesforce/moirai-1.0-R-small",
-                low_cpu_mem_usage=True,
-                device_map="cpu",
-                torch_dtype=torch.float32,
-                trust_remote_code=True
-            )
-            
-            model = MoiraiForecast(
-                module=module,
-                prediction_length=self.prediction_length,
-                context_length=self.context_length,
-                patch_size="auto",
-                num_samples=15,
-                target_dim=1,
-                feat_dynamic_real_dim=0,
-                past_feat_dynamic_real_dim=0
-            )
-            
-            self.model_cache[model_key] = model
-            return model, "moirai"
-            
-        except Exception as e:
-            return None, None
-    
-    def predict_chronos_fast(self, pipeline: Any, data: np.ndarray) -> Optional[Dict]:
-        """Fast Chronos prediction"""
-        try:
-            context_data = data[-self.context_length:]
-            context = torch.tensor(context_data, dtype=torch.float32).unsqueeze(0)
-            
-            with torch.no_grad():
-                forecast = pipeline.predict(
-                    context=context,
-                    prediction_length=self.prediction_length,
-                    num_samples=10,
-                    temperature=1.0,
-                    top_k=50,
-                    top_p=1.0
-                )
-            
-            forecast_array = forecast[0].numpy()
-            predictions = {
-                'mean': np.median(forecast_array, axis=0),
-                'q10': np.quantile(forecast_array, 0.1, axis=0),
-                'q90': np.quantile(forecast_array, 0.9, axis=0),
-                'std': np.std(forecast_array, axis=0)
-            }
-            
-            return predictions
-            
-        except Exception as e:
-            return None
-    
-    def predict_moirai_fast(self, model: Any, data: np.ndarray) -> Optional[Dict]:
-        """Fast Moirai prediction"""
-        try:
-            from gluonts.dataset.common import ListDataset
-            
-            dataset = ListDataset([{
-                "item_id": "stock",
-                "start": "2023-01-01",
-                "target": data[-self.context_length:].tolist()
-            }], freq='D')
-            
-            predictor = model.create_predictor(
-                batch_size=1,
-                num_parallel_samples=10
-            )
-            
-            forecasts = list(predictor.predict(dataset))
-            forecast = forecasts[0]
-            
-            predictions = {
-                'mean': forecast.mean,
-                'q10': forecast.quantile(0.1),
-                'q90': forecast.quantile(0.9),
-                'std': np.std(forecast.samples, axis=0)
-            }
-            
-            return predictions
-            
-        except Exception as e:
-            return None
-
-# Initialize analyzer globally for caching
-analyzer = FastAIStockAnalyzer()
-
-def analyze_stock(stock_symbol, model_choice, investment_amount, progress=gr.Progress()):
-    """Main analysis function for Gradio"""
-    
-    progress(0.1, desc="Fetching stock data...")
-    
-    # Fetch data
-    stock_data, stock_info = analyzer.fetch_stock_data(stock_symbol)
-    
-    if stock_data is None or len(stock_data) < 50:
-        return (
-            "❌ Error: Insufficient data for analysis. Please check the stock symbol.",
-            None,
-            None,
-            "N/A",
-            "N/A"
-        )
-    
-    current_price = stock_data['Close'].iloc[-1]
-    company_name = stock_info.get('longName', stock_symbol) if stock_info else stock_symbol
-    
-    progress(0.3, desc="Loading AI model...")
-    
-    # Load model
-    model_type = "chronos" if "Chronos" in model_choice else "moirai"
-    
-    if model_type == "chronos":
-        model, loaded_type = analyzer.load_chronos_tiny()
-        model_name = "Amazon Chronos Tiny"
-    else:
-        model, loaded_type = analyzer.load_moirai_small()
-        model_name = "Salesforce Moirai Small"
-    
-    if model is None:
-        return (
-            "❌ Error: Failed to load AI model. Please try again.",
-            None,
-            None,
-            "N/A",
-            "N/A"
-        )
-    
-    progress(0.6, desc="Generating AI predictions...")
-    
-    # Generate predictions
-    if model_type == "chronos":
-        predictions = analyzer.predict_chronos_fast(model, stock_data['Close'].values)
-    else:
-        predictions = analyzer.predict_moirai_fast(model, stock_data['Close'].values)
-    
-    if predictions is None:
-        return (
-            "❌ Error: Prediction failed. Please try again.",
-            None,
-            None,
-            "N/A",
-            "N/A"
-        )
-    
-    progress(0.8, desc="Calculating investment scenarios...")
-    
-    # Analysis results
-    mean_pred = predictions['mean']
-    final_pred = mean_pred[-1]
-    week_change = ((final_pred - current_price) / current_price) * 100
-    
-    # Decision logic
-    if week_change > 5:
-        decision = "🟢 STRONG BUY"
-        explanation = "AI expects significant gains!"
-    elif week_change > 2:
-        decision = "🟢 BUY"
-        explanation = "AI expects moderate gains"
-    elif week_change < -5:
-        decision = "🔴 STRONG SELL"
-        explanation = "AI expects significant losses"
-    elif week_change < -2:
-        decision = "🔴 SELL"
-        explanation = "AI expects losses"
-    else:
-        decision = "⚪ HOLD"
-        explanation = "AI expects stable prices"
-    
-    # Create analysis text
-    analysis_text = f"""
-# 🎯 {company_name} ({stock_symbol}) Analysis
-
-## 🤖 AI RECOMMENDATION: {decision}
-**{explanation}**
-*Powered by {model_name}*
-
-## 📊 Key Metrics
-- **Current Price**: ${current_price:.2f}
-- **7-Day Prediction**: ${final_pred:.2f} ({week_change:+.2f}%)
-- **AI Confidence**: {min(100, max(50, 70 + abs(week_change) * 1.5)):.0f}%
-- **Model Used**: {model_name}
-
-## 💰 Investment Scenario (${investment_amount:,.0f})
-- **Shares**: {investment_amount/current_price:.2f}
-- **Predicted Value**: ${investment_amount + ((final_pred - current_price) * (investment_amount/current_price)):,.2f}
-- **Profit/Loss**: ${((final_pred - current_price) * (investment_amount/current_price)):+,.2f} ({week_change:+.2f}%)
-
-⚠️ **DISCLAIMER**: This is AI-generated analysis for educational purposes only. Not financial advice.
-"""
-    
-    progress(0.9, desc="Creating charts...")
-    
-    # Create chart
-    fig = go.Figure()
-    
-    # Historical data (last 30 days)
-    recent = stock_data.tail(30)
-    fig.add_trace(go.Scatter(
-        x=recent.index, 
-        y=recent['Close'],
-        mode='lines', 
-        name='Historical Price',
-        line=dict(color='blue', width=2)
-    ))
-    
-    # Predictions
-    future_dates = pd.date_range(
-        start=stock_data.index[-1] + pd.Timedelta(days=1),
-        periods=7, 
-        freq='D'
-    )
-    
-    fig.add_trace(go.Scatter(
-        x=future_dates, 
-        y=mean_pred,
-        mode='lines+markers', 
-        name='AI Prediction',
-        line=dict(color='red', width=3),
-        marker=dict(size=8)
-    ))
-    
-    # Confidence bands
-    if 'q10' in predictions and 'q90' in predictions:
-        fig.add_trace(go.Scatter(
-            x=future_dates.tolist() + future_dates[::-1].tolist(),
-            y=predictions['q90'].tolist() + predictions['q10'][::-1].tolist(),
-            fill='toself', 
-            fillcolor='rgba(255,0,0,0.1)',
-            line=dict(color='rgba(255,255,255,0)'),
-            name='Confidence Range', 
-            showlegend=True
-        ))
-    
-    fig.update_layout(
-        title=f"{stock_symbol} - AI Stock Forecast",
-        xaxis_title="Date", 
-        yaxis_title="Price ($)",
-        height=500, 
-        showlegend=True,
-        template="plotly_white"
-    )
-    
-    progress(1.0, desc="Analysis complete!")
-    
-    # Create summary metrics
-    day_change = stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[-2]
-    day_change_pct = (day_change / stock_data['Close'].iloc[-2]) * 100
-    
-    current_metrics = f"${current_price:.2f} ({day_change_pct:+.2f}%)"
-    prediction_metrics = f"${final_pred:.2f} ({week_change:+.2f}%)"
-    
-    return (
-        analysis_text,
-        fig,
-        decision,
-        current_metrics,
-        prediction_metrics
-    )
-
-# Create Gradio interface[1][2][9][12]
-with gr.Blocks(
-    theme=gr.themes.Soft(),
-    title="⚡ Fast AI Stock Predictor",
-    css="footer {visibility: hidden}"
-) as demo:
-    
-    gr.HTML("""
-    <div style="text-align: center; padding: 20px;">
-        <h1>⚡ Fast AI Stock Predictor</h1>
-        <p><strong>🤖 Powered by Amazon Chronos & Salesforce Moirai</strong></p>
-        <p style="color: #666; font-size: 14px;">⚠️ Educational use only - Not financial advice</p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.HTML("<h3>🎯 Configuration</h3>")
-            
-            stock_input = gr.Dropdown(
-                choices=["AAPL", "GOOGL", "MSFT", "TSLA", "AMZN", "META", "NFLX", "NVDA"],
-                value="AAPL",
-                label="Select Stock",
-                allow_custom_value=True,
-                info="Choose from popular stocks or enter custom symbol"
-            )
-            
-            model_input = gr.Radio(
-                choices=["🚀 Chronos (Fast)", "🎯 Moirai (Accurate)"],
-                value="🚀 Chronos (Fast)",
-                label="AI Model",
-                info="Chronos: Faster | Moirai: More accurate"
-            )
-            
-            investment_input = gr.Slider(
-                minimum=500,
-                maximum=50000,
-                value=5000,
-                step=500,
-                label="Investment Amount ($)",
-                info="Amount to analyze for profit/loss scenarios"
-            )
-            
-            analyze_btn = gr.Button(
-                "🚀 Analyze Stock", 
-                variant="primary", 
-                size="lg"
-            )
-            
-        with gr.Column(scale=2):
-            gr.HTML("<h3>📊 Results</h3>")
-            
-            with gr.Row():
-                current_price_display = gr.Textbox(
-                    label="Current Price", 
-                    interactive=False,
-                    container=True
-                )
-                prediction_display = gr.Textbox(
-                    label="7-Day Prediction", 
-                    interactive=False,
-                    container=True
-                )
-                decision_display = gr.Textbox(
-                    label="AI Decision", 
-                    interactive=False,
-                    container=True
-                )
-    
-    with gr.Row():
-        analysis_output = gr.Markdown(
-            label="Analysis Report",
-            value="Click 'Analyze Stock' to generate AI-powered analysis..."
-        )
-    
-    with gr.Row():
-        chart_output = gr.Plot(
-            label="Price Chart & Predictions",
-            container=True
-        )
-    
-    # Event handlers
-    analyze_btn.click(
-        fn=analyze_stock,
-        inputs=[stock_input, model_input, investment_input],
-        outputs=[
-            analysis_output, 
-            chart_output, 
-            decision_display,
-            current_price_display,
-            prediction_display
-        ]
-    )
-    
-    # Examples
-    gr.Examples(
-        examples=[
-            ["AAPL", "🚀 Chronos (Fast)", 5000],
-            ["TSLA", "🎯 Moirai (Accurate)", 10000],
-            ["GOOGL", "🚀 Chronos (Fast)", 2500],
-        ],
-        inputs=[stock_input, model_input, investment_input],
-        label="Try these examples:"
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import warnings
+import time
+import gc
+import os
+import torch
+from datetime import datetime, timedelta
+from typing import Optional, Dict, Any, Tuple
+
+warnings.filterwarnings('ignore')
+
+# Environment optimizations for Hugging Face Spaces
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+os.environ['HF_HUB_DISABLE_PROGRESS_BARS'] = '1'
+os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
+os.environ['TRANSFORMERS_VERBOSITY'] = 'error'
+
+if torch.cuda.is_available():
+    torch.cuda.empty_cache()
+torch.set_num_threads(min(4, os.cpu_count() or 1))
+
+class FastAIStockAnalyzer:
+    """Optimized AI Stock Analyzer for Gradio"""
+    
+    def __init__(self):
+        self.context_length = 32
+        self.prediction_length = 7
+        self.device = "cpu"
+        self.model_cache = {}
+        
+    def fetch_stock_data(self, symbol: str, period: str = "6mo") -> Tuple[Optional[pd.DataFrame], Optional[Dict]]:
+        """Fetch stock data with error handling"""
+        try:
+            ticker = yf.Ticker(symbol)
+            data = ticker.history(period=period, interval="1d", 
+                                actions=False, auto_adjust=True, 
+                                back_adjust=False, repair=False)
+            
+            if data.empty:
+                return None, None
+                
+            try:
+                info = {
+                    'longName': ticker.info.get('longName', symbol),
+                    'sector': ticker.info.get('sector', 'Unknown'),
+                    'marketCap': ticker.info.get('marketCap', 0)
+                }
+            except:
+                info = {'longName': symbol, 'sector': 'Unknown', 'marketCap': 0}
+            
+            return data, info
+            
+        except Exception as e:
+            return None, None
+    
+    def load_chronos_tiny(self) -> Tuple[Optional[Any], str]:
+        """Load Chronos model with caching"""
+        model_key = "chronos_tiny"
+        
+        if model_key in self.model_cache:
+            return self.model_cache[model_key], "chronos"
+        
+        try:
+            from chronos import ChronosPipeline
+            
+            pipeline = ChronosPipeline.from_pretrained(
+                "amazon/chronos-t5-tiny",
+                device_map="cpu",
+                torch_dtype=torch.float32,
+                low_cpu_mem_usage=True,
+                trust_remote_code=True
+            )
+            
+            self.model_cache[model_key] = pipeline
+            return pipeline, "chronos"
+            
+        except Exception as e:
+            return None, None
+    
+    def load_moirai_small(self) -> Tuple[Optional[Any], str]:
+        """Load Moirai model with caching"""
+        model_key = "moirai_small"
+        
+        if model_key in self.model_cache:
+            return self.model_cache[model_key], "moirai"
+        
+        try:
+            from uni2ts.model.moirai import MoiraiForecast, MoiraiModule
+            
+            module = MoiraiModule.from_pretrained(
+                "Salesforce/moirai-1.0-R-small",
+                low_cpu_mem_usage=True,
+                device_map="cpu",
+                torch_dtype=torch.float32,
+                trust_remote_code=True
+            )
+            
+            model = MoiraiForecast(
+                module=module,
+                prediction_length=self.prediction_length,
+                context_length=self.context_length,
+                patch_size="auto",
+                num_samples=15,
+                target_dim=1,
+                feat_dynamic_real_dim=0,
+                past_feat_dynamic_real_dim=0
+            )
+            
+            self.model_cache[model_key] = model
+            return model, "moirai"
+            
+        except Exception as e:
+            return None, None
+    
+    def predict_chronos_fast(self, pipeline: Any, data: np.ndarray) -> Optional[Dict]:
+        """Fast Chronos prediction"""
+        try:
+            context_data = data[-self.context_length:]
+            context = torch.tensor(context_data, dtype=torch.float32).unsqueeze(0)
+            
+            with torch.no_grad():
+                forecast = pipeline.predict(
+                    context=context,
+                    prediction_length=self.prediction_length,
+                    num_samples=10,
+                    temperature=1.0,
+                    top_k=50,
+                    top_p=1.0
+                )
+            
+            forecast_array = forecast[0].numpy()
+            predictions = {
+                'mean': np.median(forecast_array, axis=0),
+                'q10': np.quantile(forecast_array, 0.1, axis=0),
+                'q90': np.quantile(forecast_array, 0.9, axis=0),
+                'std': np.std(forecast_array, axis=0)
+            }
+            
+            return predictions
+            
+        except Exception as e:
+            return None
+    
+    def predict_moirai_fast(self, model: Any, data: np.ndarray) -> Optional[Dict]:
+        """Fast Moirai prediction"""
+        try:
+            from gluonts.dataset.common import ListDataset
+            
+            dataset = ListDataset([{
+                "item_id": "stock",
+                "start": "2023-01-01",
+                "target": data[-self.context_length:].tolist()
+            }], freq='D')
+            
+            predictor = model.create_predictor(
+                batch_size=1,
+                num_parallel_samples=10
+            )
+            
+            forecasts = list(predictor.predict(dataset))
+            forecast = forecasts[0]
+            
+            predictions = {
+                'mean': forecast.mean,
+                'q10': forecast.quantile(0.1),
+                'q90': forecast.quantile(0.9),
+                'std': np.std(forecast.samples, axis=0)
+            }
+            
+            return predictions
+            
+        except Exception as e:
+            return None
+
+# Initialize analyzer globally for caching
+analyzer = FastAIStockAnalyzer()
+
+def analyze_stock(stock_symbol, model_choice, investment_amount, progress=gr.Progress()):
+    """Main analysis function for Gradio"""
+    
+    progress(0.1, desc="Fetching stock data...")
+    
+    # Fetch data
+    stock_data, stock_info = analyzer.fetch_stock_data(stock_symbol)
+    
+    if stock_data is None or len(stock_data) < 50:
+        return (
+            "❌ Error: Insufficient data for analysis. Please check the stock symbol.",
+            None,
+            None,
+            "N/A",
+            "N/A"
+        )
+    
+    current_price = stock_data['Close'].iloc[-1]
+    company_name = stock_info.get('longName', stock_symbol) if stock_info else stock_symbol
+    
+    progress(0.3, desc="Loading AI model...")
+    
+    # Load model
+    model_type = "chronos" if "Chronos" in model_choice else "moirai"
+    
+    if model_type == "chronos":
+        model, loaded_type = analyzer.load_chronos_tiny()
+        model_name = "Amazon Chronos Tiny"
+    else:
+        model, loaded_type = analyzer.load_moirai_small()
+        model_name = "Salesforce Moirai Small"
+    
+    if model is None:
+        return (
+            "❌ Error: Failed to load AI model. Please try again.",
+            None,
+            None,
+            "N/A",
+            "N/A"
+        )
+    
+    progress(0.6, desc="Generating AI predictions...")
+    
+    # Generate predictions
+    if model_type == "chronos":
+        predictions = analyzer.predict_chronos_fast(model, stock_data['Close'].values)
+    else:
+        predictions = analyzer.predict_moirai_fast(model, stock_data['Close'].values)
+    
+    if predictions is None:
+        return (
+            "❌ Error: Prediction failed. Please try again.",
+            None,
+            None,
+            "N/A",
+            "N/A"
+        )
+    
+    progress(0.8, desc="Calculating investment scenarios...")
+    
+    # Analysis results
+    mean_pred = predictions['mean']
+    final_pred = mean_pred[-1]
+    week_change = ((final_pred - current_price) / current_price) * 100
+    
+    # Decision logic
+    if week_change > 5:
+        decision = "🟢 STRONG BUY"
+        explanation = "AI expects significant gains!"
+    elif week_change > 2:
+        decision = "🟢 BUY"
+        explanation = "AI expects moderate gains"
+    elif week_change < -5:
+        decision = "🔴 STRONG SELL"
+        explanation = "AI expects significant losses"
+    elif week_change < -2:
+        decision = "🔴 SELL"
+        explanation = "AI expects losses"
+    else:
+        decision = "⚪ HOLD"
+        explanation = "AI expects stable prices"
+    
+    # Create analysis text
+    analysis_text = f"""
+# 🎯 {company_name} ({stock_symbol}) Analysis
+
+## 🤖 AI RECOMMENDATION: {decision}
+**{explanation}**
+*Powered by {model_name}*
+
+## 📊 Key Metrics
+- **Current Price**: ${current_price:.2f}
+- **7-Day Prediction**: ${final_pred:.2f} ({week_change:+.2f}%)
+- **AI Confidence**: {min(100, max(50, 70 + abs(week_change) * 1.5)):.0f}%
+- **Model Used**: {model_name}
+
+## 💰 Investment Scenario (${investment_amount:,.0f})
+- **Shares**: {investment_amount/current_price:.2f}
+- **Predicted Value**: ${investment_amount + ((final_pred - current_price) * (investment_amount/current_price)):,.2f}
+- **Profit/Loss**: ${((final_pred - current_price) * (investment_amount/current_price)):+,.2f} ({week_change:+.2f}%)
+
+⚠️ **DISCLAIMER**: This is AI-generated analysis for educational purposes only. Not financial advice.
+"""
+    
+    progress(0.9, desc="Creating charts...")
+    
+    # Create chart
+    fig = go.Figure()
+    
+    # Historical data (last 30 days)
+    recent = stock_data.tail(30)
+    fig.add_trace(go.Scatter(
+        x=recent.index, 
+        y=recent['Close'],
+        mode='lines', 
+        name='Historical Price',
+        line=dict(color='blue', width=2)
+    ))
+    
+    # Predictions
+    future_dates = pd.date_range(
+        start=stock_data.index[-1] + pd.Timedelta(days=1),
+        periods=7, 
+        freq='D'
+    )
+    
+    fig.add_trace(go.Scatter(
+        x=future_dates, 
+        y=mean_pred,
+        mode='lines+markers', 
+        name='AI Prediction',
+        line=dict(color='red', width=3),
+        marker=dict(size=8)
+    ))
+    
+    # Confidence bands
+    if 'q10' in predictions and 'q90' in predictions:
+        fig.add_trace(go.Scatter(
+            x=future_dates.tolist() + future_dates[::-1].tolist(),
+            y=predictions['q90'].tolist() + predictions['q10'][::-1].tolist(),
+            fill='toself', 
+            fillcolor='rgba(255,0,0,0.1)',
+            line=dict(color='rgba(255,255,255,0)'),
+            name='Confidence Range', 
+            showlegend=True
+        ))
+    
+    fig.update_layout(
+        title=f"{stock_symbol} - AI Stock Forecast",
+        xaxis_title="Date", 
+        yaxis_title="Price ($)",
+        height=500, 
+        showlegend=True,
+        template="plotly_white"
+    )
+    
+    progress(1.0, desc="Analysis complete!")
+    
+    # Create summary metrics
+    day_change = stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[-2]
+    day_change_pct = (day_change / stock_data['Close'].iloc[-2]) * 100
+    
+    current_metrics = f"${current_price:.2f} ({day_change_pct:+.2f}%)"
+    prediction_metrics = f"${final_pred:.2f} ({week_change:+.2f}%)"
+    
+    return (
+        analysis_text,
+        fig,
+        decision,
+        current_metrics,
+        prediction_metrics
+    )
+
+# Create Gradio interface[1][2][9][12]
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    title="⚡ Fast AI Stock Predictor",
+    css="footer {visibility: hidden}"
+) as demo:
+    
+    gr.HTML("""
+    <div style="text-align: center; padding: 20px;">
+        <h1>⚡ Fast AI Stock Predictor</h1>
+        <p><strong>🤖 Powered by Amazon Chronos & Salesforce Moirai</strong></p>
+        <p style="color: #666; font-size: 14px;">⚠️ Educational use only - Not financial advice</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML("<h3>🎯 Configuration</h3>")
+            
+            stock_input = gr.Dropdown(
+                choices=["AAPL", "GOOGL", "MSFT", "TSLA", "AMZN", "META", "NFLX", "NVDA"],
+                value="AAPL",
+                label="Select Stock",
+                allow_custom_value=True,
+                info="Choose from popular stocks or enter custom symbol"
+            )
+            
+            model_input = gr.Radio(
+                choices=["🚀 Chronos (Fast)", "🎯 Moirai (Accurate)"],
+                value="🚀 Chronos (Fast)",
+                label="AI Model",
+                info="Chronos: Faster | Moirai: More accurate"
+            )
+            
+            investment_input = gr.Slider(
+                minimum=500,
+                maximum=50000,
+                value=5000,
+                step=500,
+                label="Investment Amount ($)",
+                info="Amount to analyze for profit/loss scenarios"
+            )
+            
+            analyze_btn = gr.Button(
+                "🚀 Analyze Stock", 
+                variant="primary", 
+                size="lg"
+            )
+            
+        with gr.Column(scale=2):
+            gr.HTML("<h3>📊 Results</h3>")
+            
+            with gr.Row():
+                current_price_display = gr.Textbox(
+                    label="Current Price", 
+                    interactive=False,
+                    container=True
+                )
+                prediction_display = gr.Textbox(
+                    label="7-Day Prediction", 
+                    interactive=False,
+                    container=True
+                )
+                decision_display = gr.Textbox(
+                    label="AI Decision", 
+                    interactive=False,
+                    container=True
+                )
+    
+    with gr.Row():
+        analysis_output = gr.Markdown(
+            label="Analysis Report",
+            value="Click 'Analyze Stock' to generate AI-powered analysis..."
+        )
+    
+    with gr.Row():
+        chart_output = gr.Plot(
+            label="Price Chart & Predictions",
+            container=True
+        )
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=analyze_stock,
+        inputs=[stock_input, model_input, investment_input],
+        outputs=[
+            analysis_output, 
+            chart_output, 
+            decision_display,
+            current_price_display,
+            prediction_display
+        ]
+    )
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["AAPL", "🚀 Chronos (Fast)", 5000],
+            ["TSLA", "🎯 Moirai (Accurate)", 10000],
+            ["GOOGL", "🚀 Chronos (Fast)", 2500],
+        ],
+        inputs=[stock_input, model_input, investment_input],
+        label="Try these examples:"
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)