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