stages/data_collection.py

import streamlit as st

def main():
    st.title("Step 2: Data Collection :rocket:")

    st.markdown(
        """
        Data collection is the **foundation** of any NLP project. It involves gathering text data that your model will analyze and learn from. Think of it as collecting the raw materials needed to build something incredible!

        To make data collection **efficient**, follow a step-by-step approach, starting with the easiest methods before progressing to more complex options.
        """
    )
    st.divider()

    # Section 1: Pre-Existing Datasets
    st.subheader("1. Start with Pre-Existing Datasets :books:")
    st.write(
        """
        The fastest way to gather data is to use **publicly available datasets**. These are ready-made datasets curated for various NLP tasks like sentiment analysis, chatbots, or classification.
        """
    )
    st.markdown(
        """
        **Where to find datasets:**
        - [Google Dataset Search](https://datasetsearch.research.google.com/)
        - Platforms like [Kaggle](https://www.kaggle.com/), **UCI Machine Learning Repository**, or [Hugging Face Datasets](https://huggingface.co/datasets).
        - NLP libraries like **NLTK** and **SpaCy** provide built-in datasets.

        **Why start here?**
        - Saves time and effort.
        - Datasets are often clean, well-structured, and ready to use.

        **Example:**
        - For a sentiment analysis project, the **IMDB Reviews dataset** from Kaggle contains pre-labeled positive and negative movie reviews.
        """
    )
    st.divider()

    # Section 2: Using APIs
    st.subheader("2. Use APIs for Structured Data :wrench:")
    st.write(
        """
        If pre-existing datasets don't meet your needs, the next step is to use **APIs** to gather structured and specific data. APIs allow you to access text from platforms like social media, news websites, or product reviews.
        """
    )
    st.markdown(
        """
        **Popular APIs for text data:**
        - **Social Media:** Twitter API, Reddit API.
        - **News Articles:** NewsAPI, NYTimes API.
        - **E-Commerce Reviews:** Amazon API, Yelp API.
        - **Beginner-Friendly:** [RapidAPI](https://rapidapi.com/) for easy integration.

        **Why use APIs?**
        - Provides clean, structured data.
        - Allows you to collect **targeted data** like tweets containing specific keywords or customer reviews for a product.

        **Example:**
        - Use the **Twitter API** to collect tweets related to a trending topic for sentiment analysis.
        """
    )
    st.divider()

    # Section 3: Web Scraping
    st.subheader("3. Web Scraping for Unstructured Data :spider_web:")
    st.write(
        """
        If APIs are unavailable or insufficient, you can turn to **web scraping**. This involves extracting text data directly from websites like blogs, news portals, or e-commerce platforms.
        """
    )
    st.markdown(
        """
        **Tools for Web Scraping:**
        - **BeautifulSoup** and **Scrapy** for static websites.
        - **Selenium** for dynamic websites that require interaction (like loading reviews).

        **Why use web scraping?**
        - Offers control over the exact data you collect.
        - Ideal for extracting large amounts of **unstructured data**.

        **Challenges to consider:**
        - Websites may block scrapers with CAPTCHAs or security restrictions.
        - Data might require extra cleaning and preprocessing.

        **Example:**
        - Scrape customer reviews from an e-commerce site or extract news articles for a topic modeling project.
        """
    )
    st.divider()

    # Section 4: Manual Data Collection
    st.subheader("4. Collect Data on Your Own :scroll:")
    st.write(
        """
        When existing datasets, APIs, or web scraping don't provide the data you need, the final step is to **collect or generate the data manually**. This method is more time-consuming but gives you full control over the data quality and labeling.
        """
    )
    st.markdown(
        """
        **Methods for manual data collection:**
        - **Surveys & Feedback:** Gather text responses directly from users.
        - **Manual Annotation:** Label raw text data yourself for classification tasks.
        - **Synthetic Data Generation:** Use text augmentation techniques like paraphrasing or synonym replacement.

        **Why use this method?**
        - Provides fully customized and niche data.
        - Essential for projects requiring **domain-specific** or highly tailored data.

        **Example:**
        - Manually collecting medical advice conversations for building a healthcare chatbot.
        """
    )
    st.divider()

    # Summary Section
    st.subheader("Summary: Data Collection Hierarchy :ruler:")
    st.markdown(
        """
        To collect high-quality data efficiently, follow this structured approach:

        1. **Start with Pre-Existing Datasets**: Ready-made, clean, and fast to use.
        2. **Use APIs**: Gather structured and specific data.
        3. **Web Scraping**: Extract unstructured data when APIs fall short.
        4. **Manual Collection**: Create or annotate custom data for full control.

        **Friendly Tip :bulb::**
        Always start simple! Use pre-existing datasets or APIs first, and only move to web scraping or manual collection if necessary. The goal is to get **clean, diverse, and meaningful data** because **better data = better NLP models**.
        """
    )
    st.divider()
main()