https://github.com/shrutakeerti/assignments-combined
This is the repo to keep all the assigments combined I did till now
https://github.com/shrutakeerti/assignments-combined
ai aiml api ml streamlit web
Last synced: 2 months ago
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This is the repo to keep all the assigments combined I did till now
- Host: GitHub
- URL: https://github.com/shrutakeerti/assignments-combined
- Owner: Shrutakeerti
- Created: 2024-09-27T09:12:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-17T21:08:16.000Z (over 1 year ago)
- Last Synced: 2025-07-01T01:44:21.541Z (12 months ago)
- Topics: ai, aiml, api, ml, streamlit, web
- Language: Jupyter Notebook
- Homepage:
- Size: 8.76 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Programming Test: Learning Activations in Neural Networks
## Overview
This project is a Breast Cancer Prediction System that utilizes a Neural Network model to predict whether a tumor is malignant or benign based on input features. Additionally, the project integrates a chatbot assistant to guide users in using the system or answer basic questions.
## Features
- **Breast Cancer Prediction**: Enter tumor features to get a prediction (malignant or benign).
- **Interactive Chatbot**: A simple rule-based chatbot to assist users with the prediction process.
- **Streamlit UI**: User-friendly interface built using Streamlit for both prediction and chatbot interactions.
- **Flask API**: Backend server powered by Flask that handles prediction requests and chatbot interactions.
## Table of Contents
- Project Structure
- Installation
- Usage
- Endpoints
- Technology Stack
- Screenshots
- License
- Contributors
- Contact
## Project Structure
```bash
.
├── app.py # Flask API for prediction and chatbot
├── streamlit_app.py # Streamlit app for prediction UI and chatbot interface
├── model.pth # Pretrained PyTorch model
├── README.md # Project readme file
└── requirements.txt # List of dependencies
```
## Installation
### 1.Clone the repository:
```bash
git clone https://github.com/your-username/breast-cancer-prediction-chatbot.git
cd breast-cancer-prediction-chatbot
```
### 2. Set up a virtual environment:
#### For Windows:
```bash
python -m venv venv
venv\Scripts\activate
```
#### For macOS/Linux:
```bash
python3 -m venv venv
source venv/bin/activate
```
### 3. Install dependencies:
```bash
pip install -r requirements.txt
```
#### Download the pretrained model:
#### Make sure you have the model.pth file in the project directory.
## Usage
### Start the Flask API:
```bash
python app.py
```
### This will start the Flask server on http://127.0.0.1:5000. The server handles both prediction and chatbot routes.
### Run the Streamlit App.In a separate terminal, run the following command to launch the Streamlit app:
```bash
streamlit run streamlit_app.py
```
## Endpoints
### Prediction Endpoint:
- **URL**: `/predict`
- **Method**: `POST`
- **Request Body**:
```json
{
"features": [feature_1, feature_2, ..., feature_31]
}
```
- **Response**:
```json
{
"prediction": "malignant" or "benign"
}
```
### Chatbot Endpoint:
- **URL**: `/chatbot`
- **Method**: `POST`
- **Request Body**:
```json
{
"message": "user's message"
}
```
- **Response**:
```json
{
"response": "chatbot's response"
}
```
## Technology Stack
- **Frontend**: Streamlit (for UI)
- **Backend**: Flask (API)
- **ML Model**: PyTorch
- **Libraries**:
- `torch`: For neural network model
- `pandas`, `numpy`: Data manipulation
- `sklearn`: Data preprocessing (StandardScaler)
- `requests`: API requests
## Screenshot
.png)
## This is deployed using streamlit