https://github.com/bevinaa/medical-chatbot-application
An End-to-End Medical Chatbot powered by generative AI, designed to provide accurate responses to medical queries. Built using Flask, Cohere’s Language Model, and Pinecone for Vector Storage.
https://github.com/bevinaa/medical-chatbot-application
artificial-intelligence cohere-ai html-css-javascript jupyter-notebook llm-training machine-learning pinecone-db python
Last synced: 7 months ago
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An End-to-End Medical Chatbot powered by generative AI, designed to provide accurate responses to medical queries. Built using Flask, Cohere’s Language Model, and Pinecone for Vector Storage.
- Host: GitHub
- URL: https://github.com/bevinaa/medical-chatbot-application
- Owner: Bevinaa
- Created: 2024-11-06T08:43:46.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-11-06T09:20:01.000Z (12 months ago)
- Last Synced: 2025-01-24T02:15:29.954Z (9 months ago)
- Topics: artificial-intelligence, cohere-ai, html-css-javascript, jupyter-notebook, llm-training, machine-learning, pinecone-db, python
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **End-to-End Medical Chatbot Application**
## **Overview**
This repository contains an **End-to-End Medical Chatbot** powered by **Generative AI**, designed to provide accurate and contextually relevant responses to medical queries. Built using **Flask**, **Cohere’s Language Model**, and **Pinecone** for vector storage, this chatbot can answer a wide range of health-related questions and offer context-aware follow-ups. The chatbot remembers previous queries to offer more personalized responses.
---
## **Key Features**
- **Generative AI-Powered Responses**: Uses **Cohere’s Language Model** to generate medical answers.
- **Context-Aware Conversations**: The chatbot can maintain the context of the conversation, enabling more accurate follow-up answers.
- **Efficient Document Retrieval**: Integrated with **Pinecone**, a vector database, for fast and scalable document search.
- **Flask Web Application**: A user-friendly web interface powered by Flask for seamless interaction.
- **Easy Setup**: Simple installation steps and easy API key configuration for deployment.
---
## **Technologies Used**
- **Flask**: Web framework for building the backend.
- **Cohere**: Language model for generating answers.
- **Pinecone**: Vector database for document retrieval and storage.
- **HTML/CSS/Bootstrap**: Frontend for the chat interface.
- **Python**: Programming language for implementing AI logic and web server.
---
## **Pre-requisites**
To get started with the project, you’ll need the following:
- **Python 3.7+** (preferably in a virtual environment)
- **Cohere API Key**: For generating AI responses.
- **Pinecone API Key**: For vector search and document retrieval.
- **Flask**: To run the web application.
---
## **Setting Up the Project**
Follow the steps below to set up and run the chatbot on your local machine:
### 1. Clone the Repository
Start by cloning this repository to your local machine:
```bash
git clone (https://github.com/Bevinaa/Medical-Chatbot-Application)
cd End-to-end-Medical-Chatbot-Generative-AI
```
### 2. Set Up a Virtual Environment (Recommended)
You can set up a virtual environment to isolate dependencies for this project:
"venv" is used here as environment name
```bash
python -m venv venv
```
Activate the virtual environment:
- **Windows**:
```bash
venv\Scripts\activate
```
- **Mac/Linux**:
```bash
source venv/bin/activate
```
### 3. Install Dependencies
Install the required dependencies using `pip`:
```bash
pip install -r requirements.txt
```
### 4. Create API Keys
You will need two API keys: **Cohere API Key** for the language model and **Pinecone API Key** for vector storage. Here's how to get them:
#### Cohere API Key:
1. Go to [Cohere API](https://cohere.ai/) and create an account if you haven’t already.
2. Once logged in, navigate to the **API Keys** section and generate a new API key.
3. Copy the API key for use in the project.
#### Pinecone API Key:
1. Visit [Pinecone](https://www.pinecone.io/) and create an account.
2. After logging in, navigate to **API Keys** from your dashboard.
3. Generate an API key and copy it.
### 5. Configure API Keys
Create a `.env` file in the root directory of the project and add the following lines, replacing the placeholders with your actual API keys:
```env
COHERE_API_KEY = your_cohere_api_key
PINECONE_API_KEY = your_pinecone_api_key
```
### 6. Running the Flask Application
Once you've set up the environment and configured the API keys, you can run the Flask app with the following command:
```bash
python app.py
```
The chatbot should now be running at `http://127.0.0.1:5000/`. Open this URL in your browser to interact with the chatbot.
---
## **How to Use the Chatbot**
1. Visit the main page (`/`).
2. You’ll see a user interface where you can type your health-related question.
3. After submitting your question, the chatbot will generate a response based on the medical documents it retrieves from Pinecone.
You can ask follow-up questions, and the chatbot will maintain the context of your conversation to provide more relevant answers.
---
## **Project Structure**
- **app.py**: Main Python script that runs the Flask application.
- **chat.html**: HTML template for the chatbot user interface.
- **requirements.txt**: List of Python dependencies for the project.
- **src/**: Contains helper functions and code for document embedding and retrieval.
- **.env**: Configuration file to store sensitive API keys.
---
## **Contributing**
If you would like to contribute to this project, feel free to fork the repository, make changes, and submit a pull request.
Make sure to follow the standard guidelines for Python projects and ensure that all dependencies are correctly listed in the `requirements.txt` file.
---
## **Contact**
For any questions, issues, or support, feel free to reach out:
Author: **Bevina**
Email: [bevina2110@gmail.com](mailto:bevina2110@gmail.com)
---
## **Output**
Below are some screenshots showing the chatbot interface and how it responds to user queries.
### 1. Chat Interface
This is the main chat interface where users can ask health-related questions.
### 2. Context-Aware Follow-up
An example showing the chatbot’s ability to maintain conversation context across multiple questions.
### 3. Sample Response
An example of a response generated by the chatbot based on a user query.
---