Create app.py

app.py

@@ -0,0 +1,129 @@
+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()