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import gradio as gr
from transformers import pipeline
import json
# Initialize NLP pipeline
ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
def analyze_event(text):
try:
# Process text with NER pipeline
ner_results = ner_pipeline(text)
# Group entities
entities = {
"people": [],
"organizations": [],
"locations": [],
"hashtags": [word for word in text.split() if word.startswith('#')]
}
for item in ner_results:
if item["entity"].endswith("PER"):
entities["people"].append(item["word"])
elif item["entity"].endswith("ORG"):
entities["organizations"].append(item["word"])
elif item["entity"].endswith("LOC"):
entities["locations"].append(item["word"])
# Calculate confidence
confidence = min(1.0, (
0.2 * bool(entities["people"]) +
0.2 * bool(entities["organizations"]) +
0.3 * bool(entities["locations"]) +
0.3 * bool(entities["hashtags"])
))
return {
"text": text,
"entities": entities,
"confidence": confidence,
"verification_needed": confidence < 0.6
}
except Exception as e:
return {"error": str(e)}
# Create Gradio interface with custom CSS and HTML
css = """
.container { max-width: 800px; margin: auto; padding: 20px; }
.results { padding: 20px; border: 1px solid #ddd; border-radius: 8px; margin-top: 20px; }
.confidence-high { color: #22c55e; font-weight: bold; }
.confidence-low { color: #f97316; font-weight: bold; }
.entity-section { margin: 15px 0; }
.alert-warning { background: #fff3cd; padding: 10px; border-radius: 5px; margin: 10px 0; }
.alert-success { background: #d1fae5; padding: 10px; border-radius: 5px; margin: 10px 0; }
"""
def format_results(analysis_result):
if "error" in analysis_result:
return f"<div style='color: red'>Error: {analysis_result['error']}</div>"
confidence_class = "confidence-high" if analysis_result["confidence"] >= 0.6 else "confidence-low"
html = f"""
<div class="results">
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 20px;">
<h3 style="margin: 0;">Analysis Results</h3>
<div>
Confidence Score: <span class="{confidence_class}">{int(analysis_result['confidence'] * 100)}%</span>
</div>
</div>
{f'''
<div class="alert-warning">
⚠️ <strong>Verification Required:</strong> Low confidence score detected. Please verify the extracted information.
</div>
''' if analysis_result["verification_needed"] else ''}
<div class="entity-section">
<h4>👤 People Detected</h4>
<ul>{''.join(f'<li>{person}</li>' for person in analysis_result['entities']['people']) or '<li>None detected</li>'}</ul>
</div>
<div class="entity-section">
<h4>🏢 Organizations</h4>
<ul>{''.join(f'<li>{org}</li>' for org in analysis_result['entities']['organizations']) or '<li>None detected</li>'}</ul>
</div>
<div class="entity-section">
<h4>📍 Locations</h4>
<ul>{''.join(f'<li>{loc}</li>' for loc in analysis_result['entities']['locations']) or '<li>None detected</li>'}</ul>
</div>
<div class="entity-section">
<h4># Hashtags</h4>
<ul>{''.join(f'<li>{tag}</li>' for tag in analysis_result['entities']['hashtags']) or '<li>None detected</li>'}</ul>
</div>
{f'''
<div class="alert-success">
✅ <strong>Event Validated:</strong> The extracted information meets confidence thresholds.
</div>
''' if not analysis_result["verification_needed"] else ''}
</div>
"""
return html
demo = gr.Interface(
fn=lambda text: format_results(analyze_event(text)),
inputs=[
gr.Textbox(
label="Event Text",
placeholder="Enter text to analyze (e.g., 'John from Tech Corp. is attending the meeting in Washington, DC #tech')",
lines=3
)
],
outputs=gr.HTML(),
title="DoD Event Analysis System",
description="Analyze text to extract entities, assess confidence, and identify key event information.",
css=css,
theme=gr.themes.Soft(),
examples=[
["John from Tech Corp. is attending the meeting in Washington, DC tomorrow #tech"],
["Sarah Johnson and Mike Smith from Defense Systems Inc. are conducting training in Norfolk, VA #defense #training"],
["Team meeting at headquarters with @commander_smith #briefing"]
]
)
if __name__ == "__main__":
demo.launch() |