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Upload render_report.py
Browse files- render_report.py +128 -0
render_report.py
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import json
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import matplotlib.pyplot as plt
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import networkx as nx
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# Load the OSINT JSON data
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with open("data.json", "r", encoding="utf-8") as f:
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data = json.load(f)
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# Generate sentiment pie chart
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labels = list(data["sentiment_overview"].keys())
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sizes = list(data["sentiment_overview"].values())
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colors = ['#4CAF50', '#FFC107', '#F44336']
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fig, ax = plt.subplots()
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ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=colors)
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ax.axis('equal')
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plt.title('Sentiment Overview')
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sentiment_chart = "/mnt/data/sentiment_pie_chart.png"
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plt.savefig(sentiment_chart, bbox_inches='tight')
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plt.close()
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# Generate network graph
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G = nx.Graph()
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for cluster in data["network_analysis"]["clusters"]:
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nodes = cluster["nodes"]
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for i in range(len(nodes)):
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G.add_node(nodes[i])
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for j in range(i + 1, len(nodes)):
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G.add_edge(nodes[i], nodes[j])
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plt.figure(figsize=(8, 6))
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pos = nx.spring_layout(G, seed=42)
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node_colors = ['lightblue' if 'Trump' in node else 'salmon' for node in G.nodes()]
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nx.draw(G, pos, with_labels=True, node_color=node_colors, edge_color='gray', node_size=1200, font_size=8)
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plt.title("Network Influence Graph")
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network_chart = "/mnt/data/network_graph.png"
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plt.savefig(network_chart, bbox_inches='tight')
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plt.close()
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# Generate HTML report
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html_content = f"""
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>OSINT Intelligence Report</title>
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<style>
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body {{
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font-family: Arial, sans-serif;
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margin: 40px;
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background-color: #f9f9f9;
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}}
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h1 {{
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color: #2c3e50;
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text-align: center;
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}}
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h2 {{
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color: #34495e;
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border-bottom: 2px solid #ccc;
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padding-bottom: 5px;
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}}
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.section {{
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margin-bottom: 30px;
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}}
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.image {{
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text-align: center;
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margin: 20px 0;
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}}
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</style>
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</head>
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<body>
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<h1>OSINT Intelligence Report</h1>
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<div class="section">
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<h2>Summary</h2>
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<p>{data["summary"]}</p>
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</div>
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<div class="section">
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<h2>Top Topics</h2>
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<p>{', '.join(data["top_topics"])}</p>
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</div>
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<div class="section">
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<h2>Notable Users</h2>
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{''.join([f"<p><strong>{u['username']} ({u['type']}):</strong> {u['activity_summary']}</p>" for u in data["notable_users"]])}
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</div>
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<div class="section">
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<h2>Network Analysis</h2>
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{''.join([f"<p><strong>{c['label']}:</strong> {c['summary']}</p>" for c in data["network_analysis"]["clusters"]])}
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</div>
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<div class="section">
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<h2>Sentiment Overview</h2>
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<p>Positive: {data['sentiment_overview']['positive']}%</p>
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<p>Neutral: {data['sentiment_overview']['neutral']}%</p>
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<p>Negative: {data['sentiment_overview']['negative']}%</p>
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<div class="image">
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<img src="sentiment_pie_chart.png" width="500">
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</div>
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</div>
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<div class="section">
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<h2>Network Influence Graph</h2>
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<div class="image">
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<img src="network_graph.png" width="600">
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</div>
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</div>
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<div class="section">
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<h2>Risk Flags</h2>
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<ul>
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{''.join([f"<li>{flag}</li>" for flag in data["risk_flags"]])}
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</ul>
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</div>
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<div class="section">
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<h2>Timestamp Range</h2>
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<p>From: {data["timestamp_range"]["from"]}</p>
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<p>To: {data["timestamp_range"]["to"]}</p>
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</div>
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</body>
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</html>
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"""
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with open("/mnt/data/OSINT_Report.html", "w", encoding="utf-8") as f:
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f.write(html_content)
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