Update stages/data_collection.py

stages/data_collection.py

@@ -1,5 +1,131 @@
-import streamlit as st
-
-def main():
-    st.title("Data Collection..!!")
+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()