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Healthcare-Diagnosis-Chat

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# Healthcare Diagnosis Chatbot

## Project Overview

The Healthcare Chatbot is a Python-based desktop application designed to predict potential diseases based on user-entered symptoms. It leverages natural language processing (NLP) techniques and a pretrained Named Entity Recognition (NER) model from Huggingface to extract disease names from user input.

## Project Goals

The primary goal of this project is to provide users with accurate disease predictions based on their symptoms. By utilizing advanced NLP and machine learning models, the chatbot aims to assist users in understanding potential health issues they may be experiencing.

## Technologies Used

- **Python**: Core programming language used for the application.
- **TKinter**: Python's standard GUI (Graphical User Interface) toolkit used for creating the desktop-based application.
- **Spacy**: Open-source library for NLP tasks such as tokenization and lemmatization.
- **Huggingface Transformers**: Utilized for accessing a pretrained model (`raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed`) to extract disease names from user input.
- **pip**: Python's package installer used for managing dependencies.

## Installation

To run the Healthcare Chatbot locally, follow these steps:

1. **Clone the repository**:
   ```
   git clone https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot.git
   cd Healthcare-Diagnosis-Chatbot
   ```
2. **Install dependencies**:
   ```
     pip install -r requirements.txt
   ```
3. **Download Spacy**:
   ```
    pip install -U pip setuptools wheel
    pip install -U spacy
    python -m spacy download en_core_web_sm
   ```
4. **Download and save pre-trained model from Huggingface as newsave_model**
   ```
    from transformers import pipeline
    PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed"
    ners = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)
    
    import pickle as pk
    with open('newsave_model', 'wb') as f:
        pk.dump(ners, f)
   ```
## Usage

1. **Run the Application**
   ```
   python3 ./HealthCare_App.py
   ```

2. **Enter Symptoms**
Use the GUI interface to enter symptoms. The chatbot will predict potential diseases based on the symptoms provided.
Output Image

3. **View results**
The chatbot will display the predicted diseases or health conditions corresponding to the symptoms entered.

## Application Overview
![image_1](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/1.png?raw=true)
![image_2](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/2.png?raw=true)
![image_3](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/3.png?raw=true)
![image_4](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/4.png?raw=true)

	a/README.md		b/README.md
1	# Healthcare Diagnosis Chatbot	1	# Healthcare Diagnosis Chatbot
2		2
3	## Project Overview	3	## Project Overview
4		4
5	The Healthcare Chatbot is a Python-based desktop application designed to predict potential diseases based on user-entered symptoms. It leverages natural language processing (NLP) techniques and a pretrained Named Entity Recognition (NER) model from Huggingface to extract disease names from user input.	5	The Healthcare Chatbot is a Python-based desktop application designed to predict potential diseases based on user-entered symptoms. It leverages natural language processing (NLP) techniques and a pretrained Named Entity Recognition (NER) model from Huggingface to extract disease names from user input.
6		6
7	## Project Goals	7	## Project Goals
8		8
9	The primary goal of this project is to provide users with accurate disease predictions based on their symptoms. By utilizing advanced NLP and machine learning models, the chatbot aims to assist users in understanding potential health issues they may be experiencing.	9	The primary goal of this project is to provide users with accurate disease predictions based on their symptoms. By utilizing advanced NLP and machine learning models, the chatbot aims to assist users in understanding potential health issues they may be experiencing.
10		10
11	## Technologies Used	11	## Technologies Used
12		12
13	- Python: Core programming language used for the application.	13	- Python: Core programming language used for the application.
14	- TKinter: Python's standard GUI (Graphical User Interface) toolkit used for creating the desktop-based application.	14	- TKinter: Python's standard GUI (Graphical User Interface) toolkit used for creating the desktop-based application.
15	- Spacy: Open-source library for NLP tasks such as tokenization and lemmatization.	15	- Spacy: Open-source library for NLP tasks such as tokenization and lemmatization.
16	- Huggingface Transformers: Utilized for accessing a pretrained model (`raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed`) to extract disease names from user input.	16	- Huggingface Transformers: Utilized for accessing a pretrained model (`raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed`) to extract disease names from user input.
17	- pip: Python's package installer used for managing dependencies.	17	- pip: Python's package installer used for managing dependencies.
18		18
19	## Installation	19	## Installation
20		20
21	To run the Healthcare Chatbot locally, follow these steps:	21	To run the Healthcare Chatbot locally, follow these steps:
22		22
23	1. Clone the repository:	23	1. Clone the repository:
24	```	24	```
25	git clone https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot.git	25	git clone https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot.git
26	cd Healthcare-Diagnosis-Chatbot	26	cd Healthcare-Diagnosis-Chatbot
27	```	27	```
28	2. Install dependencies:	28	2. Install dependencies:
29	```	29	```
30	pip install -r requirements.txt	30	pip install -r requirements.txt
31	```	31	```
32	3. Download Spacy:	32	3. Download Spacy:
33	```	33	```
34	pip install -U pip setuptools wheel	34	pip install -U pip setuptools wheel
35	pip install -U spacy	35	pip install -U spacy
36	python -m spacy download en_core_web_sm	36	python -m spacy download en_core_web_sm
37	```	37	```
38	4. Download and save pre-trained model from Huggingface as newsave_model	38	4. Download and save pre-trained model from Huggingface as newsave_model
39	```	39	```
40	from transformers import pipeline	40	from transformers import pipeline
41	PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed"	41	PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed"
42	ners = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)	42	ners = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)
43		43
44	import pickle as pk	44	import pickle as pk
45	with open('newsave_model', 'wb') as f:	45	with open('newsave_model', 'wb') as f:
46	pk.dump(ners, f)	46	pk.dump(ners, f)
47	```	47	```
48	## Usage	48	## Usage
49		49
50	1. Run the Application	50	1. Run the Application
51	```	51	```
52	python3 ./HealthCare_App.py	52	python3 ./HealthCare_App.py
53	```	53	```
54		54
55	2. Enter Symptoms	55	2. Enter Symptoms
56	Use the GUI interface to enter symptoms. The chatbot will predict potential diseases based on the symptoms provided.	56	Use the GUI interface to enter symptoms. The chatbot will predict potential diseases based on the symptoms provided.
57	Output Image	57	Output Image
58		58
59	3. View results	59	3. View results
60	The chatbot will display the predicted diseases or health conditions corresponding to the symptoms entered.	60	The chatbot will display the predicted diseases or health conditions corresponding to the symptoms entered.
61		61
62	## Application Overview	62	## Application Overview
63	![image_1](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/1.png)	63	![image_1](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/1.png?raw=true)
64	![image_2](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/2.png)	64	![image_2](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/2.png?raw=true)
65	![image_3](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/3.png)	65	![image_3](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/3.png?raw=true)
66	![image_4](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/4.png)	66	![image_4](https://github.com/riyajha2305/Healthcare-Diagnosis-Chatbot-MS-Hackathon/blob/main/HealthCare_ChatBot/images/4.png?raw=true)