📁 FolderScopeAI - Deep Code & Text Analyzer

A next-gen forensic scanner for folders packed with code, documentation, and more. Analyze, summarize, and report like a digital detective 🕵️‍♂️.

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🚀 How to Use

🔧 Basic Command

python main.py <folder_path>

⚙️ Available CLI Flags

Flag	Description
--html	Generate HTML report
--pdf	Generate PDF report (WeasyPrint or ReportLab)
--csv	Export report to CSV format
--log	Save a .log file of operations
--zip	Auto zip the report directory
--deep	Perform forensic scans for code smell, reuse, obfuscation, sensitive data

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🧠 Modules & Functions

📜 Text Analyzer

Located at: report_generator/text_analyzer.py

• analyze_text_file(file_path)
  ➤ Returns word count, sentence count, sentiment polarity & subjectivity

• advanced_text_insights(file_path)
  ➤ Adds reading ease score, most common words, and plagiarism matches

• perform_ner_and_sentiment(file_path)
  ➤ Named Entity Recognition + sentiment scan

• text_plagiarism_check(file_path)
  ➤ Uses TF-IDF + cosine similarity to flag reused content in the same folder

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🧬 Code Analyzer

Located at: report_generator/code_analyzer.py

• analyze_code_file(file_path)
  ➤ Detects number of functions, classes, language type, and complexity hints

• deep_code_analysis()
  ➤ Hooks into more advanced tools for structural insights

• run_bandit_scan(file_path)
  ➤ Python security audit using Bandit

• detect_sensitive_data(file_path)
  ➤ Flags tokens, API keys, secrets

• detect_code_reuse(file_path)
  ➤ Checks for signs of copied logic from other sources

• detect_obfuscated_code(file_path)
  ➤ Spots minified, encoded, or misleading structures

• analyze_code_complexity(file_path)
  ➤ Measures cyclomatic complexity, nested blocks, and potential technical debt

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🗃️ Output Formats

• .md: Full Markdown Summary Report
• .html: Stylish HTML version of the above
• .pdf: Clean printable report
• .csv: Tabular dump of file metrics
• .log: Step-by-step operations log
• .zip: Compressed report package

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🔩 Requirements

Install dependencies using pip:

pip install -r requirements.txt

Or individually:

pip install textblob spacy sklearn weasyprint markdown2
python -m textblob.download_corpora
python -m spacy download en_core_web_sm

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🌟 Example Usage

python main.py D:/ProjectPortfolio --html --csv --log --zip --deep

This will produce:

• An HTML report
• A CSV table
• A zip file with all results
• Deep scan insights

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📌 Notes

• Currently works best on .txt, .py, .js, .html, .md, .json files
• Plagiarism check compares local .txt files only
• Bandit scan requires bandit installed (pip install bandit)

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🧑‍💻 Made for:

• Researchers 🧪
• Developers 🧑‍💻
• Forensic Analysts 🕵️‍♀️
• AI Workflow Auditors 🤖

FolderScopeAI isn’t just a tool. It’s a codebase conscience.

🔧 Features

🔍 Text Analysis (text_analyzer.py)

• analyze_text_file() → Word count, sentence count, sentiment polarity and subjectivity
• advanced_text_insights() → Flesch reading ease, most common words, plagiarism detection
• perform_ner_and_sentiment() → Named Entity Recognition (NER) + sentiment analysis
• text_plagiarism_check() → Detect similarity between .txt files in same folder

🧠 Code Analysis (code_analyzer.py)

• analyze_code_file() → Line count, class/function count, comments ratio
• deep_code_analysis() → Flags long functions, duplicate lines, weird code patterns
• run_bandit_scan() → Runs Bandit security scan on Python files
• analyze_code_complexity() → Complexity and readability check (NEW!)

🕵️ Forensic Insights (forensic_analyzer.py)

• detect_sensitive_data() → Finds hardcoded keys, tokens, secrets
• detect_code_reuse() → Flags reused logic across files (GitHub-copy style)
• detect_obfuscated_code() → Detects encoded or minified suspicious code

🚀 CLI Usage

python main.py <folder_path> [--html] [--csv] [--log] [--zip] [--deep]

Flags

• --html → Export full HTML report
• --csv → Export insights to CSV
• --log → Create scan log file
• --zip → Archive the folder with timestamp
• --deep → Run forensic scans on top of analysis

📦 Installation

Make sure you’re in a virtual environment, then run:

pip install textblob
pip install spacy
pip install scikit-learn
python -m textblob.download_corpora
python -m spacy download en_core_web_sm

✅ Output

• Markdown and HTML summaries per file
• Forensic logs (if --log enabled)
• CSVs with structured results
• Optional .zip backup of the scanned folder

💬 Author

Made with ❤️ by Ijtihad Emon — AI-assisted, precision-delivered.

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Stay curious. Stay skeptical. Scan responsibly.