Spaces:
Running
Running
File size: 7,548 Bytes
04013dc 8b44a11 04013dc 8b44a11 04013dc 8b44a11 04013dc 8b44a11 04013dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import json
from io import BytesIO
import plotly.graph_objects as go
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.platypus import Image, Paragraph, Preformatted, SimpleDocTemplate, Spacer, Table, TableStyle
def _figure_to_image(figure, width=640, height=300):
"""Convert a Plotly figure to a reportlab Image. Returns None if conversion fails."""
try:
image_bytes = figure.to_image(format="png", width=width, height=height, scale=2)
return Image(BytesIO(image_bytes), width=width * 0.75, height=height * 0.75)
except Exception:
return None
def _metrics_table(rows):
table = Table(rows, hAlign="LEFT")
table.setStyle(
TableStyle(
[
("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#f3f4f6")),
("TEXTCOLOR", (0, 0), (-1, 0), colors.HexColor("#111827")),
("ALIGN", (0, 0), (-1, -1), "LEFT"),
("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#d1d5db")),
("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
]
)
)
return table
def _single_model_section(story, model_name, model_payload, styles):
prediction = "HATE SPEECH DETECTED" if model_payload["prediction"] == 1 else "NOT HATE SPEECH"
prediction_color = "#c62828" if model_payload["prediction"] == 1 else "#2e7d32"
story.append(Paragraph(f"<b>{model_name}</b>", styles["Heading3"]))
story.append(Paragraph(f"<font color='{prediction_color}'><b>{prediction}</b></font>", styles["Normal"]))
story.append(Spacer(1, 6))
rows = [
["Metric", "Value"],
["Confidence", f"{model_payload['confidence']:.1%}"],
["Not Hate Speech", f"{model_payload['probabilities'][0]:.1%}"],
["Hate Speech", f"{model_payload['probabilities'][1]:.1%}"],
["Processing Time", f"{model_payload['processing_time']:.3f}s"],
]
story.append(_metrics_table(rows))
story.append(Spacer(1, 8))
probability_chart = go.Figure(
data=[
go.Bar(
x=["Not Hate Speech", "Hate Speech"],
y=model_payload["probabilities"],
marker_color=["#66bb6a", "#ef5350"],
text=[f"{value:.1%}" for value in model_payload["probabilities"]],
textposition="auto",
)
]
)
probability_chart.update_layout(yaxis_range=[0, 1], height=300, showlegend=False, margin=dict(l=20, r=20, t=20, b=20))
chart_image = _figure_to_image(probability_chart)
if chart_image:
story.append(Paragraph("Probability Distribution", styles["Italic"]))
story.append(chart_image)
story.append(Spacer(1, 8))
token_rows = model_payload.get("token_importance") or []
if token_rows:
story.append(Paragraph("Top Important Tokens", styles["Italic"]))
token_table_rows = [["Token", "Importance"]] + [
[token_data["Token"], f"{token_data['Importance']:.4f}"] for token_data in token_rows
]
story.append(_metrics_table(token_table_rows))
story.append(Spacer(1, 8))
technical_details = model_payload.get("technical_details")
if technical_details:
story.append(Paragraph("Technical Details", styles["Italic"]))
story.append(Preformatted(json.dumps(technical_details, indent=2), styles["Code"]))
story.append(Spacer(1, 12))
def _batch_model_section(story, model_name, model_payload, styles, colorscale):
story.append(Paragraph(f"<b>{model_name}</b>", styles["Heading3"]))
story.append(Spacer(1, 4))
rows = [
["Metric", "Value"],
["F1 Score", f"{model_payload['f1_score']:.4f}"],
["Precision", f"{model_payload['precision']:.4f}"],
["Accuracy", f"{model_payload['accuracy']:.4f}"],
["Recall", f"{model_payload['recall']:.4f}"],
["Avg CPU", f"{model_payload['cpu_usage']:.2f}%"],
["Peak CPU", f"{model_payload['peak_cpu_usage']:.2f}%"],
["Avg Memory", f"{model_payload['memory_usage']:.2f} MB"],
["Peak Memory", f"{model_payload['peak_memory_usage']:.2f} MB"],
["Total Runtime", f"{model_payload['runtime']:.2f}s"],
["Avg Time/Sample", f"{model_payload['avg_time_per_sample']:.3f}s"],
]
story.append(_metrics_table(rows))
story.append(Spacer(1, 8))
confusion_matrix = model_payload.get("confusion_matrix")
if confusion_matrix is not None:
confusion_fig = go.Figure(
data=go.Heatmap(
z=confusion_matrix,
x=["Pred Not Hate", "Pred Hate"],
y=["True Not Hate", "True Hate"],
colorscale=colorscale,
text=confusion_matrix,
texttemplate="%{text}",
textfont={"size": 14},
showscale=False,
)
)
confusion_fig.update_layout(height=300, margin=dict(l=20, r=20, t=20, b=20))
confusion_image = _figure_to_image(confusion_fig)
if confusion_image:
story.append(Paragraph("Confusion Matrix", styles["Italic"]))
story.append(confusion_image)
story.append(Spacer(1, 12))
def generate_results_pdf(payload):
"""Generate a PDF from the current Streamlit results payload."""
output_buffer = BytesIO()
document = SimpleDocTemplate(
output_buffer,
pagesize=letter,
leftMargin=36,
rightMargin=36,
topMargin=36,
bottomMargin=36,
title="Hate Speech Detection Results",
)
styles = getSampleStyleSheet()
story = [
Paragraph("Hate Speech Detection Results", styles["Title"]),
Spacer(1, 8),
]
mode = payload.get("mode", "single")
if mode == "single":
story.append(Paragraph("Single Text Analysis", styles["Heading2"]))
story.append(Spacer(1, 6))
story.append(Paragraph(f"<b>Input Text:</b> {payload.get('input_text', '')}", styles["Normal"]))
rationale_text = payload.get("rationale")
if rationale_text:
story.append(Spacer(1, 4))
story.append(Paragraph(f"<b>Rationale:</b> {rationale_text}", styles["Normal"]))
story.append(Spacer(1, 10))
models = payload.get("models", {})
if "base" in models:
_single_model_section(story, "Base Bert Ensemble Results", models["base"], styles)
if "enhanced" in models:
_single_model_section(story, "Enhanced Bert Ensemble Results", models["enhanced"], styles)
elif mode == "batch":
story.append(Paragraph("Batch File Analysis", styles["Heading2"]))
story.append(Spacer(1, 6))
story.append(Paragraph(f"<b>Filename:</b> {payload.get('filename', 'Unknown')}", styles["Normal"]))
story.append(Paragraph(f"<b>Rows:</b> {payload.get('rows', 0)}", styles["Normal"]))
story.append(Spacer(1, 10))
models = payload.get("models", {})
if "base" in models:
_batch_model_section(story, "Base Bert Ensemble Results", models["base"], styles, "Blues")
if "enhanced" in models:
_batch_model_section(story, "Enhanced Bert Ensemble Results", models["enhanced"], styles, "Greens")
else:
story.append(Paragraph("No exportable results found.", styles["Normal"]))
document.build(story)
return output_buffer.getvalue()
|