Spaces:
Sleeping
Sleeping
File size: 2,386 Bytes
e0b361b 4aaabca b853527 4aaabca b853527 4aaabca b853527 4aaabca b853527 4aaabca b853527 4aaabca b853527 4aaabca b853527 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | ---
title: MedicalChatBot
emoji: 🔥
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 6.12.0
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Patient/Doctor Medication Management Chatbot
## Domain
Medication Management
## Setup Instructions
### Prerequisites
- Python 3.14.4+
- Ollama
- Gradio
### Installation
1. Install Ollama
```bash
# Mac:
brew install ollama
# Windows: Download from https://ollama.ai
# Linux:
curl -fsSL https://ollama.com/install.sh | sh
```
1. Download llama3.2:3b model:
```bash
ollama pull llama3.2:3b
```
1. Install Python packages
```bash
pip install ollama gradio
```
### Running
```bash
python your_file.py
```
## Features
- Feature 1: Answer patient questions about medications and remembers history.
- Feature 2: Provide doctors with summaries of patient inquiries to help with medication management.
- Feature 3: Suggests prompts depending on the mode selected (different prompts for doctors and patients).
- Feature 4: Export conversation history for record-keeping or further analysis.
## Technical Details
- Model: llama3.2:3b
- Framework: Ollama
## Demo
[Link to video OR screenshots]
## Known Limitations
Currently, both patient and doctor modes are on a shared user interface. This serves as a prototype to easily see how patients and healthcare professionals can leverage this tool to understand a patient's lived experience with their medication.
Additionally, the long-term memory of this tool is minimal. After several prompts, the chatbot will begin to respond with blanks because the context is overflowing.
## Future Improvements
With more time, I would build out this interface to work as two independent applications.
One for patients, and one for Healthcare professionals.
Use a more dynamic way of keeping the context of a patient's prompts, over multiple days, and without causing issues for the model. Local caching of patient's prompts in summarization form may allow a more concise version to be passed in as context. This would mean the doctor would get a more accurate idea of the patient's questions over a longer period of time. |