Create visualization.py

visualization.py

@@ -0,0 +1,197 @@
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import numpy as np
+from collections import Counter
+from typing import List, Dict, Optional
+
+from models import handle_errors, ThemeContext
+
+# Optimized Plotly Visualization System
+class PlotlyVisualizer:
+    """Enhanced Plotly visualizations"""
+    
+    @staticmethod
+    @handle_errors(default_return=None)
+    def create_sentiment_gauge(result: Dict, theme: ThemeContext) -> go.Figure:
+        """Create animated sentiment gauge"""
+        colors = theme.colors
+        
+        if result.get('has_neutral', False):
+            # Three-way gauge
+            fig = go.Figure(go.Indicator(
+                mode="gauge+number+delta",
+                value=result['pos_prob'] * 100,
+                domain={'x': [0, 1], 'y': [0, 1]},
+                title={'text': f"Sentiment: {result['sentiment']}"},
+                delta={'reference': 50},
+                gauge={
+                    'axis': {'range': [None, 100]},
+                    'bar': {'color': colors['pos'] if result['sentiment'] == 'Positive' else colors['neg']},
+                    'steps': [
+                        {'range': [0, 33], 'color': colors['neg']},
+                        {'range': [33, 67], 'color': colors['neu']},
+                        {'range': [67, 100], 'color': colors['pos']}
+                    ],
+                    'threshold': {
+                        'line': {'color': "red", 'width': 4},
+                        'thickness': 0.75,
+                        'value': 90
+                    }
+                }
+            ))
+        else:
+            # Two-way gauge
+            fig = go.Figure(go.Indicator(
+                mode="gauge+number",
+                value=result['confidence'] * 100,
+                domain={'x': [0, 1], 'y': [0, 1]},
+                title={'text': f"Confidence: {result['sentiment']}"},
+                gauge={
+                    'axis': {'range': [None, 100]},
+                    'bar': {'color': colors['pos'] if result['sentiment'] == 'Positive' else colors['neg']},
+                    'steps': [
+                        {'range': [0, 50], 'color': "lightgray"},
+                        {'range': [50, 100], 'color': "gray"}
+                    ]
+                }
+            ))
+        
+        fig.update_layout(height=400, font={'size': 16})
+        return fig
+    
+    @staticmethod
+    @handle_errors(default_return=None)
+    def create_probability_bars(result: Dict, theme: ThemeContext) -> go.Figure:
+        """Create probability bar chart"""
+        colors = theme.colors
+        
+        if result.get('has_neutral', False):
+            labels = ['Negative', 'Neutral', 'Positive']
+            values = [result['neg_prob'], result['neu_prob'], result['pos_prob']]
+            bar_colors = [colors['neg'], colors['neu'], colors['pos']]
+        else:
+            labels = ['Negative', 'Positive']
+            values = [result['neg_prob'], result['pos_prob']]
+            bar_colors = [colors['neg'], colors['pos']]
+        
+        fig = go.Figure(data=[
+            go.Bar(x=labels, y=values, marker_color=bar_colors, 
+                   text=[f'{v:.3f}' for v in values], textposition='outside')
+        ])
+        
+        fig.update_layout(
+            title="Sentiment Probabilities",
+            yaxis_title="Probability",
+            height=400,
+            showlegend=False
+        )
+        return fig
+    
+    @staticmethod
+    @handle_errors(default_return=None)
+    def create_batch_summary(results: List[Dict], theme: ThemeContext) -> go.Figure:
+        """Create batch analysis summary"""
+        colors = theme.colors
+        
+        # Count sentiments
+        sentiments = [r['sentiment'] for r in results if 'sentiment' in r and r['sentiment'] != 'Error']
+        sentiment_counts = Counter(sentiments)
+        
+        # Create pie chart
+        fig = go.Figure(data=[go.Pie(
+            labels=list(sentiment_counts.keys()),
+            values=list(sentiment_counts.values()),
+            marker_colors=[colors.get(s.lower()[:3], '#999999') for s in sentiment_counts.keys()],
+            textinfo='label+percent',
+            hole=0.3
+        )])
+        
+        fig.update_layout(
+            title=f"Batch Analysis Summary ({len(results)} texts)",
+            height=400
+        )
+        return fig
+    
+    @staticmethod
+    @handle_errors(default_return=None)
+    def create_confidence_distribution(results: List[Dict]) -> go.Figure:
+        """Create confidence distribution plot"""
+        confidences = [r['confidence'] for r in results if 'confidence' in r and r['sentiment'] != 'Error']
+        if not confidences:
+            return go.Figure()
+        
+        fig = go.Figure(data=[go.Histogram(
+            x=confidences,
+            nbinsx=20,
+            marker_color='skyblue',
+            opacity=0.7
+        )])
+
+        fig.update_layout(
+            title="Confidence Distribution",
+            xaxis_title="Confidence Score",
+            yaxis_title="Frequency",
+            height=400
+        )
+        return fig
+    
+    @staticmethod
+    @handle_errors(default_return=None)
+    def create_history_dashboard(history: List[Dict], theme: ThemeContext) -> go.Figure:
+        """Create comprehensive history dashboard"""
+        if len(history) < 2:
+            return go.Figure()
+        
+        # Create subplots
+        fig = make_subplots(
+            rows=2, cols=2,
+            subplot_titles=['Sentiment Timeline', 'Confidence Distribution', 
+                           'Language Distribution', 'Sentiment Summary'],
+            specs=[[{"secondary_y": False}, {"secondary_y": False}],
+                   [{"type": "pie"}, {"type": "bar"}]]
+        )
+        
+        # Extract data
+        indices = list(range(len(history)))
+        pos_probs = [item.get('pos_prob', 0) for item in history]
+        confidences = [item['confidence'] for item in history]
+        sentiments = [item['sentiment'] for item in history]
+        languages = [item.get('language', 'en') for item in history]
+        
+        # Sentiment timeline
+        colors_map = {'Positive': theme.colors['pos'], 'Negative': theme.colors['neg'], 'Neutral': theme.colors['neu']}
+        colors = [colors_map.get(s, '#999999') for s in sentiments]
+        
+        fig.add_trace(
+            go.Scatter(x=indices, y=pos_probs, mode='lines+markers', 
+                      marker=dict(color=colors, size=8),
+                      name='Positive Probability'),
+            row=1, col=1
+        )
+        
+        # Confidence distribution
+        fig.add_trace(
+            go.Histogram(x=confidences, nbinsx=10, name='Confidence'),
+            row=1, col=2
+        )
+        
+        # Language distribution
+        lang_counts = Counter(languages)
+        fig.add_trace(
+            go.Pie(labels=list(lang_counts.keys()), values=list(lang_counts.values()),
+                   name="Languages"),
+            row=2, col=1
+        )
+        
+        # Sentiment summary
+        sent_counts = Counter(sentiments)
+        sent_colors = [colors_map.get(k, '#999999') for k in sent_counts.keys()]
+        fig.add_trace(
+            go.Bar(x=list(sent_counts.keys()), y=list(sent_counts.values()),
+                   marker_color=sent_colors),
+            row=2, col=2
+        )
+        
+        fig.update_layout(height=800, showlegend=False)
+        return fig