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import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import json
from typing import Dict, List, Any, Union
import torch
def display_json_as_table(data: Union[Dict, List]):
"""Display JSON data as a formatted table"""
if isinstance(data, list):
# Convert list of dicts to dataframe if possible
try:
df = pd.DataFrame(data)
st.dataframe(df)
except:
# Fall back to JSON display
st.json(data)
else:
# Convert dict to dataframe with keys and values as columns
try:
df = pd.DataFrame(list(data.items()), columns=['Key', 'Value'])
st.dataframe(df)
except:
# Fall back to JSON display
st.json(data)
def format_sentiment_result(result: List[Dict]):
"""Format sentiment analysis result for display"""
if not result:
return {}
# Get the first result (usually there's only one)
item = result[0]
# Create a dataframe for display
df = pd.DataFrame({
'Label': [item['label']],
'Score': [round(item['score'] * 100, 2)],
})
# Add visual indicator based on sentiment
is_positive = item['label'].lower() == 'positive'
color = 'green' if is_positive else 'red'
emoji = '😃' if is_positive else '😞'
return {
'dataframe': df,
'color': color,
'emoji': emoji,
'score': round(item['score'] * 100, 2)
}
def plot_sentiment_gauge(score: float, color: str):
"""Create a gauge chart for sentiment score"""
fig = px.pie(
values=[score, 100-score],
names=['Score', ''],
color_discrete_sequence=[color, '#F0F2F6'],
hole=0.7,
)
fig.update_layout(
annotations=[dict(text=f"{score}%", x=0.5, y=0.5, font_size=20, showarrow=False)],
showlegend=False,
margin=dict(l=20, r=20, t=20, b=20),
height=200,
)
return fig
def format_ner_results(ner_results: List[Dict]):
"""Format NER results for display"""
# Convert to DataFrame for easier display
if not ner_results:
return pd.DataFrame()
entities_data = []
for entity in ner_results:
entities_data.append({
'Entity': entity['word'],
'Type': entity['entity_group'],
'Score': round(entity['score'] * 100, 2),
'Start': entity['start'],
'End': entity['end']
})
return pd.DataFrame(entities_data)
def highlight_entities_in_text(text: str, entities: List[Dict]):
"""Highlight entities in text for display"""
if not entities:
return text
# Sort entities by start position in reverse order
# to avoid index shifting when replacing
sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
# Convert text to HTML with highlights
html_text = text
for entity in sorted_entities:
start = entity['start']
end = entity['end']
entity_text = text[start:end]
entity_type = entity['entity_group']
# Define color based on entity type
color_map = {
'PER': '#FFD700', # Person - Gold
'ORG': '#98FB98', # Organization - Pale Green
'LOC': '#ADD8E6', # Location - Light Blue
'MISC': '#FFA07A' # Miscellaneous - Light Salmon
}
color = color_map.get(entity_type, '#D3D3D3') # Default to light gray
# Replace text with highlighted version
highlight = f'<span style="background-color: {color}; padding: 0px 2px; border-radius: 3px;" title="{entity_type} ({round(entity["score"]*100)}%)">{entity_text}</span>'
html_text = html_text[:start] + highlight + html_text[end:]
return html_text
def plot_similarity_heatmap(query: str, texts: List[str], similarities: List[float]):
"""Create a bar chart for similarity scores"""
# Create a DataFrame with the data
df = pd.DataFrame({
'Text': texts,
'Similarity': similarities
})
# Sort by similarity score in descending order
df = df.sort_values('Similarity', ascending=False)
# Create the bar chart
fig = px.bar(
df,
x='Similarity',
y='Text',
orientation='h',
title=f'Similarity to: "{query}"',
labels={'Similarity': 'Cosine Similarity', 'Text': 'Corpus Text'},
range_x=[0, 1]
)
fig.update_layout(
height=300,
margin=dict(l=20, r=20, t=40, b=20),
)
return fig
def display_text_with_answer(context: str, answer: Dict):
"""Display context text with highlighted answer"""
if not answer or 'start' not in answer or 'end' not in answer:
return context
start = answer['start']
end = answer['end']
answer_text = answer['answer']
# Replace the answer with a highlighted version
highlighted_text = (
context[:start] +
f'<span style="background-color: #FFFF00; font-weight: bold;">{answer_text}</span>' +
context[end:]
)
return highlighted_text
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