https://github.com/agituts/ollama-vision-model-enhanced
This Streamlit application allows users to upload images and engage in interactive conversations about them using the Ollama Vision Model (llama3.2-vision). The app provides a user-friendly interface for image analysis, combining visual inputs with natural language processing to deliver detailed and context-aware responses.
https://github.com/agituts/ollama-vision-model-enhanced
ollama vision
Last synced: 5 months ago
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This Streamlit application allows users to upload images and engage in interactive conversations about them using the Ollama Vision Model (llama3.2-vision). The app provides a user-friendly interface for image analysis, combining visual inputs with natural language processing to deliver detailed and context-aware responses.
- Host: GitHub
- URL: https://github.com/agituts/ollama-vision-model-enhanced
- Owner: agituts
- Created: 2024-11-10T07:53:49.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-11-11T14:10:28.000Z (over 1 year ago)
- Last Synced: 2025-06-23T10:51:30.644Z (about 1 year ago)
- Topics: ollama, vision
- Language: Python
- Homepage:
- Size: 42 KB
- Stars: 13
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Enhanced Image Analysis with Ollama Vision Model
This Streamlit app allows users to analyze images using the Ollama Vision Model, with features such as conversation management.
## Requirements
- Python 3.x
- Streamlit
- Ollama
- Pillow
## Setup
[](https://www.youtube.com/watch?v=sdulVogM2aQ)
1. **Clone the repository**:
```bash
git clone https://github.com/agituts/ollama-vision-model-enhanced.git
cd ollama-vision-model-enhanced
```
2. **Set Up a Virtual Environmentt (Recommended)**:
Create a virtual environment to isolate project dependencies.
- On Windows
```bash
python -m venv venv
venv\Scripts\activate
```
- On macOS/Linux
```bash
python3 -m venv venv
source venv/bin/activate
```
4. **Install Python Dependencies**:
Install the required Python packages using pip:
```bash
pip install -r requirements.txt
```
If requirements.txt is missing or incomplete, install the packages manually:
```bash
pip install streamlit ollama Pillow
```
5. **Install and Set Up Ollama**:
- Download and Install Ollama: Visit https://ollama.com/ and follow the installation instructions for your operating system.
- Verify Ollama Installation:
```bash
ollama version
```
6. **Pull the Required Ollama Model**:
- Pull the llama2:3b-vision model using Ollama:
```bash
ollama pull llama2:3b-vision
```
7. **Start the Ollama server**:
- Start the Ollama server to enable communication with the model:
```bash
ollama serve
```
Tips:
- Keep this terminal window open as the server needs to run continuously. You may open a new terminal window for the next steps.
- Also, in Windows, you will need to exit any instance of Ollama running in the taskbar.
9. **Launch the App**:
- In a new terminal window (with your virtual environment activated), navigate to the project directory if you're not already there:
```bash
cd ollama-vision-model-enhanced
```
- Launch the app:
```bash
streamlit run app.py
```
This command will start the Streamlit server and open the app in your default web browser. If it doesn't open automatically, you can manually visit http://localhost:8501 in your browser.
## Usage
### Basic Operations:
- Upload an Image: Use the file uploader to select and upload an image (PNG, JPG, or JPEG).
- Add Context (Optional): In the sidebar under "Conversation Management", you can add any relevant context for the conversation.
- Enter Prompts: Use the chat input at the bottom of the app to ask questions or provide prompts related to the uploaded image.
- View Responses: The app will display the AI assistant's responses based on the image analysis and your prompts.
### Conversation Management
- Save Conversations: Conversations are saved automatically and can be managed from the sidebar under "Previous Conversations".
- Load Conversations: Load previous conversations by clicking the folder icon (📂) next to the conversation title.
- Edit Titles: Edit conversation titles by clicking the pencil icon (✏️) and saving your changes.
- Delete Conversations: Delete individual conversations using the trash icon (🗑️) or delete all conversations using the "Delete All Conversations" button.
## Troubleshooting
### Issue: Ollama Model Not Found
Symptoms:
- Errors indicating the model cannot be found.
- The app fails to generate responses.
Solution:
- Ensure you've pulled the correct model with the exact name used in the code.
- Double-check the model name in the process_image_and_text function:
```python
# Verify model name in code
response = ollama.chat(
model='llama3.2-vision',
)
```
### Issue: Connection Error with Ollama Server
Symptoms:
- Errors related to connecting to the Ollama server.
- The app is unable to process image and text prompts.
Solution:
- Ensure the Ollama server is running in a terminal (ollama serve).
- Verify there are no firewall restrictions blocking communication.
- Restart the Ollama server if necessary.
### Issue: Missing Python Packages
Symptoms:
- Import errors when running the app (e.g., ModuleNotFoundError).
- The app fails to start due to missing packages.
Solution:
- Ensure all dependencies are installed:
```bash
pip install -r requirements.txt
```
- If using a virtual environment, ensure it's activated when installing packages.
### Issue: Streamlit App Not Starting
Symptoms:
- Terminal shows errors when running streamlit run app.py.
- The app doesn't open in the browser.
Solution:
- Verify that you're in the correct directory (ollama-vision-model-enhanced).
- Ensure app.py exists in the directory.
- Check for syntax errors or typos in app.py.
## Additional Tips
Ollama Server:
- The Ollama server needs to run continuously while you're using the app.
- If you close the terminal or the server stops, restart it with:
```bash
ollama serve
```
Running on a Different Port:
- If you need to run the Streamlit app on a different port:
```bash
streamlit run app.py --server.port
```
Stopping the App:
- To stop the Streamlit app, press Ctrl+C in the terminal where it's running.
Updating the App Code:
- If you make changes to app.py, Streamlit will prompt you to rerun the app. Click "Rerun" or press R in the terminal.