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| FROM python:3.7 | ||
| WORKDIR /titanic-prediction | ||
| COPY . . | ||
| RUN python3 -m pip install -r requirements.txt | ||
| RUN python3 source/components/data_loading.py | ||
| EXPOSE 9993 | ||
| ENTRYPOINT ["streamlit","run"] | ||
| CMD ["app.py"] |
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| <style> | ||
| .glowing-text { | ||
| font-family: 'Arial Black', sans-serif; | ||
| font-size: 48px; | ||
| text-align: center; | ||
| animation: glowing 2s infinite; | ||
| } | ||
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| @keyframes glowing { | ||
| 0% { color: #FF9933; } /* Saffron color */ | ||
| 25% { color: #FFFFFF; } /* White color */ | ||
| 50% { color: #128807; } /* Green color */ | ||
| 75% { color: #0000FF; } /* Blue color */ | ||
| 100% { color: #FF9933; } /* Saffron color */ | ||
| } | ||
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| </style> | ||
| <div align='center'> | ||
| <h1><p class="glowing-text">⛵ Titanic Survival Prediction ⛵</p></h1> | ||
| </div> | ||
| <div align='center'> | ||
| <img src='https://github.com/chakka-guna-sekhar-venkata-chennaiah/pOtH_hOlEs_DeTeCtOr/assets/110555361/fd81a688-7a56-4f1b-b6c3-3de17062a025' width="1000" height="500"> | ||
| </div> | ||
| <br> | ||
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| ## 1. :hammer_and_wrench: Requirements | ||
| <div align='center'> | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/2.8-green?style=for-the-badge&logo=python&logoColor=%233776AB&label=Python&labelColor=%23FFFFFF&color=%23008000">  | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/1.22.0-green?style=for-the-badge&logo=streamlit&logoColor=%23FF4B4B&label=Streamlit&labelColor=%23FFFFFF&color=%23008000">  | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/1.2.2-green?style=for-the-badge&logo=scikit-learn&logoColor=%23F7931E&label=scikit-learn&labelColor=%23FFFFFF&color=%23008000">  | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/1.23.5-green?style=for-the-badge&logo=numpy&logoColor=%23013243&label=numpy&labelColor=%23FFFFFF&color=%23008000">  | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/1.5.2-green?style=for-the-badge&logo=pandas&logoColor=%23150458&label=pandas&labelColor=%23FFFFFF&color=%23008000">  | ||
| <img alt="Static Badge" src="https://img.shields.io/badge/20.10.21-green?style=for-the-badge&logo=docker&logoColor=%23150458&label=docker&labelColor=%23FFFFFF&color=%23008000"> | ||
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| </div> | ||
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| <br> | ||
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| ## 2. 📝 __Description__ | ||
| The __Titanic Survival Prediction__ project aims to develop an intelligent predictive model capable of estimating the likelihood of passengers' survival aboard the ill-fated Titanic ship. Leveraging advanced machine learning techniques, the system analyzes various passenger attributes and characteristics to forecast whether an individual would have survived the tragic event. | ||
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| This project finds relevance for history enthusiasts, researchers, and anyone intrigued by the Titanic's story. By harnessing the power of data-driven predictions, it offers a unique perspective into the factors that might have influenced survival outcomes. The __Titanic Survival Prediction__ project holds particular significance for those interested in understanding historical events through a modern analytical lens, shedding light on possible determinants of survival while commemorating the human narratives from that era. | ||
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| The repository is organized in a modular fashion, adhering to industry-level coding standards. Working with this repository ensures a professional and structured approach to code development. | ||
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| ## 3. 🌲 __Project Directory Structure__ | ||
| ```bash | ||
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| ├── Dockerfile | ||
| ├── README.md | ||
| ├── app.py | ||
| ├── deep.mp4 | ||
| ├── notebook | ||
| │ └── titanic.csv | ||
| ├── requirements.txt | ||
| ├── setup.py | ||
| ├── source | ||
| │ ├── __init__.py | ||
| │ ├── components | ||
| │ │ ├── __init__.py | ||
| │ │ ├── data_loading.py | ||
| │ │ ├── model_training.py | ||
| │ │ └── transformation.py | ||
| │ ├── exception.py | ||
| │ ├── loggers.py | ||
| │ ├── pipelines | ||
| │ │ ├── __init__.py | ||
| │ │ └── testing_pipeline.py | ||
| │ └── utlis.py | ||
| └── static | ||
| ├── boat.jpeg | ||
| ├── notsurvived.jpeg | ||
| └── survived.png | ||
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| ``` | ||
| 1. __Dockerfile__: This file contains the instructions to build a Docker image for your project, facilitating easy deployment and reproducibility. | ||
| 2. __README.md__: The README file provides essential information about the project, including its description, usage instructions, and deployment details. | ||
| 3. __app.py__: This file contains the code for the user interface of your __Titanic Survival Prediction System__. | ||
| 4. __notebook__: This directory stores the notebook containing the dataset __titanic.csv__ | ||
| 5. __requirements.txt__: The requirements file lists all the dependencies and their versions necessary for your project. These can be easily installed using the provided command. | ||
| 6. __setup.py__: This file is typically used for package installation and distribution if you plan to package your project for distribution. | ||
| 7. __source__: The source directory holds the core components of your project: | ||
| + __components__: Contains various Python modules like __data_loading.py__, __model_training.py__, and __transformation.py__, responsible for different aspects of the machine learning pipeline. | ||
| + __exception.py__: This file may contain custom exception classes for handling errors or exceptions specific to your project. | ||
| + __loggers.py__: Contains code related to logging and log handling. | ||
| + __pipelines.py__: This directory likely includes a __testing_pipeline.py__ module that defines a pipeline for testing your models. | ||
| + __utils.py__: Contains utility functions or helper code utilized throughout the project. | ||
| 8. __static__: This directory holds static files such as images (__boat.jpeg__, __notsurvived.jgeg__ and __survived.png__), which are potentially used in the user interface or documentation. | ||
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| ## 4. :gear: __Run Locally__ | ||
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| Clone the project | ||
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| ```bash | ||
| git clone https://github.com/software-babooi/ideal-potato-oibsip/tree/main/t3-titanic-prediction | ||
| ``` | ||
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| Go to the project directory | ||
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| ```bash | ||
| cd my-project | ||
| ``` | ||
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| Install dependencies | ||
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| ```bash | ||
| python3 -m pip install -r requirements.txt | ||
| ``` | ||
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| Initiate training | ||
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| ```bash | ||
| python3 source/components/data_loading.py | ||
| ``` | ||
| Initiating user interface | ||
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| ```bash | ||
| python3 -m streamlit run app.py | ||
| ``` | ||
| ## 5. :ship: __Deployment via Pulling the Docker Image__ | ||
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| Before proceeding with the deployment using Docker, it is essential to ensure that port 9994 is available on your system. The Docker image has been built using this specified port number. If you wish to change the port number, follow the deployment process using Docker as outlined below. This will help avoid conflicts and ensure seamless communication between the Docker container and your system. | ||
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| + __Pulling the image from my registry__: | ||
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| <p>         Before proceeding with pulling the image, kindly take note that the size of the image is approximately 2GB.</p> | ||
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| ```bash | ||
| docker pull guna1/codsoft-t3-titanic-prediction:latest | ||
| ``` | ||
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| + __Running the image__: | ||
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| ```bash | ||
| docker run -p 9992:8501 guna1/codsoft-t3-titanic-prediction:latest | ||
| ``` | ||
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| ## 6. :ship: __Deployment using Docker__ | ||
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| To implement the deployment of this project in Docker and change the port number, please follow the instructions below: | ||
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| 1. __Check Available Ports__: | ||
| First, check the available ports on your system to find one that is not in use. | ||
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| 2. __Replace Port Number in Dockerfile__: | ||
| In the Dockerfile, locate the line with "EXPOSE 9994" (where 9999 is the default port). Replace this number with the desired port number from step 1. | ||
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| <p>         By following these instructions, you can set the Docker container to use your desired system port, ensuring smooth deployment and communication without conflicts. | ||
| </p> | ||
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| :rotating_light: The following Docker deployment consumes approximately 2.00GB of the Docker disk image. | ||
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| + __Build the docker image__: | ||
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| <p>          Execute the following command to build the Docker image, replacing tagname with an appropriate tag: | ||
| </p> | ||
| ```bash | ||
| docker build -t tagname . | ||
| ``` | ||
| + __Run the Docker Container__: | ||
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| <p>          Start the Docker container using the built image with the following command: | ||
| Starting the image | ||
| </p> | ||
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| ```bash | ||
| docker run -p (your_desired_port):8501 tagname:latest | ||
| ``` | ||
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| + __Pushing the Image to a Docker Registry__: | ||
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| <p>          To avoid redeploying the image each time, you can push it to your Docker registry. Before pushing, make sure you are logged in to your Docker account using: | ||
| </p> | ||
| ```bash | ||
| docker login | ||
| ``` | ||
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| <p>Next, push the image to your registry with:</p> | ||
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| ```bash | ||
| docker push account_name/image_name:latest | ||
| ``` | ||
| This process allows you to store and retrieve the image from the registry during future deployments, making it more efficient and accessible. | ||
| ## 7. 📽 __Demo__ | ||
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| import streamlit as st | ||
| import base64 | ||
| from source.utlis import Pclass_converter, embark_converter | ||
| from source.pipelines.testing_pipeline import custom_data,prediction_pipeline | ||
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| st.set_page_config(layout="wide",initial_sidebar_state="expanded", | ||
| page_icon='⛵',page_title='Titanic Survival Prediction') | ||
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| def get_video_base64(video_path): | ||
| with open(video_path, "rb") as file: | ||
| video_bytes = file.read() | ||
| base64_encoded = base64.b64encode(video_bytes).decode("utf-8") | ||
| return base64_encoded | ||
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| video_path = "deep.mp4" | ||
| video_base64 = get_video_base64(video_path) | ||
| video_html = f""" | ||
| <style> | ||
| #myVideo {{ | ||
| position: fixed; | ||
| right: 0; | ||
| bottom: 0; | ||
| min-width: 100%; | ||
| min-height: 100%; | ||
| }} | ||
| .content {{ | ||
| position: fixed; | ||
| bottom: 0; | ||
| background: rgba(0, 0, 0, 0.5); | ||
| color: #f1f1f1; | ||
| width: 100%; | ||
| padding: 20px; | ||
| }} | ||
| </style> | ||
| <video autoplay loop muted id="myVideo"> | ||
| <source type="video/mp4" src="data:video/mp4;base64,{video_base64}"> | ||
| </video> | ||
| """ | ||
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| st.markdown(video_html, unsafe_allow_html=True) | ||
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| # Define custom style for the glowing text | ||
| glowing_text_style = ''' | ||
| <style> | ||
| .glowing-text { | ||
| font-family: 'Arial Black', sans-serif; | ||
| font-size: 48px; | ||
| text-align: center; | ||
| animation: glowing 2s infinite; | ||
| } | ||
| @keyframes glowing { | ||
| 0% { color: #FF9933; } /* Saffron color */ | ||
| 25% { color: #FFFFFF; } /* White color */ | ||
| 50% { color: #128807; } /* Green color */ | ||
| 75% { color: #0000FF; } /* Blue color */ | ||
| 100% { color: #FF9933; } /* Saffron color */ | ||
| } | ||
| </style> | ||
| ''' | ||
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| # Display the glowing text using st.markdown | ||
| st.markdown(glowing_text_style, unsafe_allow_html=True) | ||
| st.markdown(f'<p class="glowing-text">⛵ Titanic Survival Prediction ⛵</p>', unsafe_allow_html=True) | ||
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| st.image("static/boat.jpeg", use_column_width=True) | ||
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| st.subheader('Fill the Form 📄') | ||
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| Pclass=st.selectbox('Choose the passenger class:',['first-class','second-class','third-class']) | ||
| Pclass=Pclass_converter(Pclass) | ||
| Sex=st.selectbox('Choose the passenger gender:',['male','female']) | ||
| Age=st.number_input("Enter the passenger's age:") | ||
| Sibsip=st.number_input('How many siblings/spouses does passenger have?') | ||
| Parch=st.number_input('How many children/parents does passenger have?') | ||
| Fare=st.number_input("Enter the passenger's fare:") | ||
| Embarked=st.selectbox('Choose the passenger embarkation',['Cherbourg','Queenstown','Southampton']) | ||
| Embarked=embark_converter(Embarked) | ||
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| data=custom_data(Pclass,Sex,Age,Sibsip,Parch,Fare,Embarked) | ||
| pred_df=data.get_data_as_a_dataframe() | ||
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| pred_pipe=prediction_pipeline() | ||
| final_pred=pred_pipe.predict(pred_df) | ||
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| if st.button('Predict'): | ||
| if final_pred[0]==0: | ||
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| st.image('static/notsurvived.jpeg') | ||
| elif final_pred[0]==1: | ||
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| st.image('static/survived.png') | ||
| st.write(""" | ||
| ---\n | ||
| Made with ❤️ by Chakka Guna Sekhar Venkata Chennaiah. | ||
| """) |
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