🧠 Local LLM Analyst

A local, privacy-focused tool for multimodal data analysis using LLMs via Ollama.

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📦 Requirements

• Python 3.8+
• Ollama
• Models: llama3, llava (for image analysis)
• streamlit

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✨ Features

• Analyze text files (CSV, JSON, TXT)
• Basic image analysis (requires multimodal model)
• Works completely offline
• No API keys required
• Simple, intuitive interface

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🚀 Installation

1. Install Ollama

Download Ollama from ollama.ai/download and follow the instructions for your OS:

• macOS:

  brew install ollama

• Linux (Ubuntu/Debian):

curl -fsSL https://ollama.com/install.sh | sh

-Windows: Use the Windows installer from the official site:

2. Download Required Models

Open a terminal and run:

ollama pull llama3   # For text analysis  
ollama pull llava    # For image analysis (multimodal)

• run the model:

ollama run llama3

3. Clone the Repository

git clone https://github.com/AndreLiar/LocalAnalyst
cd LocalLlamaAnalyst

4. Install Python Dependencies

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

pip install -r requirements.txt

###NB: in the repo there is a folder test that contains different data type format supported you can use it to explore the tools or load your own data

5. Run the App

streamlit run app.py

Then open http://localhost:8501 in your browser.

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💡 Example Prompts

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🧠 Example Prompts

📊 For Data Files (CSV, JSON, TXT)

🔍 Exploration / Overview

• "Summarize this data."
• "What are the key columns and how many records are there?"
• "What is the age range in this dataset?"
• "List the unique cities present in the data."

📊 Statistical Analysis

• "What is the average age of individuals?"
• "Count the number of people per city."
• "Find the oldest and youngest individuals."
• "Calculate the standard deviation of the age column."

💡 Insights / Trends

• "Identify any trends or patterns in this dataset."
• "What insights can we draw from this data?"
• "Are there any outliers in the age column?"
• "Suggest 3 key takeaways based on this data."

🧹 Data Cleaning Checks

• "Are there any missing or inconsistent values?"
• "Which columns have the most repeated values?"
• "Is this data normalized or clean?"

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🖼️ For Images (using llava)

👁️ Basic Understanding

• "Describe the contents of this image."
• "What is happening in this picture?"
• "List all visible objects."

🧠 Contextual Analysis

• "What could be the context or purpose of this image?"
• "What does this image represent?"
• "What emotions or themes are suggested?"

📈 Specific Use Cases

• "Is this chart going up or down?"
• "What kind of business chart is this?"
• "Can you extract insights from this visual?"

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