Update stages/problem_statement.py

stages/problem_statement.py

@@ -1,3 +1,121 @@
 import streamlit as st
 
-st.title("Problem Statement..!!")
+def main():
+    st.title("Step 1: Problem Statement Definition 	:mag:")
+
+    st.markdown(
+        """
+        Every successful NLP project begins with a **clear and well-defined problem statement**. This step lays the groundwork by identifying what you aim to solve, why it matters, and how you plan to approach it.
+        
+        A well-defined problem statement keeps your project focused and aligned with its goals.
+        """
+    )
+    st.divider()
+
+    # Section 1: Understand the Business or Use Case
+    st.subheader("1. Understand the Business or Use Case 	:globe_with_meridians:")
+    st.write(
+        """
+        Before jumping into solutions, take time to **understand the context** of the problem. Whether you're building a chatbot, performing sentiment analysis, or classifying text, you must align your project with the **real-world needs** of the business or end-users.
+        """
+    )
+    st.markdown(
+        """
+        **Steps to Understand the Use Case:**
+        - Ask **what problem** you are solving.
+        - Determine **who benefits** from the solution (users, stakeholders).
+        - Identify **why it matters**: What value does solving this problem provide?
+
+        **Example:**
+        - Business Problem: "Customers struggle to get quick answers on our website."
+        - NLP Solution: Build a **customer support chatbot** to automate responses to common queries.
+        """
+    )
+    st.divider()
+
+    # Section 2: Define the Scope
+    st.subheader("2. Define the Scope 	:sunrise_over_mountains:")
+    st.write(
+        """
+        Clearly defining the **scope** of your problem is critical to avoid unnecessary complexity and keep the project manageable.
+        
+        Scope helps you identify **what's included** in the problem and what isn't.
+        """
+    )
+    st.markdown(
+        """
+        **Questions to Define Scope:**
+        - What **specific goals** are we targeting? (e.g., classify sentiment as positive or negative)
+        - Are there any **limitations**? (e.g., only working with English text)
+        - What **data sources** are needed? (e.g., customer reviews, social media posts)
+
+        **Example:**
+        - Problem Scope: "Analyze customer reviews to detect positive, negative, or neutral sentiments for English-language text only."
+        """
+    )
+    st.divider()
+
+    # Section 3: Identify Key Metrics
+    st.subheader("3. Identify Key Metrics 	:1234:")
+    st.write(
+        """
+        To evaluate the success of your NLP project, define **key metrics** that measure performance and ensure your model meets expectations.
+        
+        Metrics provide a way to **quantify progress** and compare different approaches.
+        """
+    )
+    st.markdown(
+        """
+        **Common NLP Metrics:**
+        - **Accuracy**: Proportion of correct predictions.
+        - **F1-Score**: Balance between precision and recall.
+        - **BLEU Score**: For evaluating text generation models.
+        - **Perplexity**: To assess language models.
+
+        **Example:**
+        - "For a sentiment analysis model, aim for an F1-score of **85% or higher** on the test dataset."
+        """
+    )
+    st.divider()
+
+    # Section 4: Formulate the Problem as an NLP Task
+    st.subheader("4. Formulate the Problem as an NLP Task :robot_face:")
+    st.write(
+        """
+        Once the problem is defined and scoped, formulate it as a specific **NLP task**. This step bridges the gap between the problem statement and the technical solution.
+        """
+    )
+    st.markdown(
+        """
+        **Common NLP Tasks:**
+        - **Text Classification**: Categorize text into predefined classes (e.g., spam detection).
+        - **Named Entity Recognition (NER)**: Identify entities like names, dates, or locations.
+        - **Sentiment Analysis**: Detect emotions like positive, negative, or neutral sentiment.
+        - **Text Summarization**: Summarize long documents into concise versions.
+
+        **Example:**
+        - Problem: "Categorize customer complaints into relevant topics."
+        - NLP Task: **Text Classification**.
+        """
+    )
+    st.divider()
+
+    # Summary Section
+    st.subheader("Summary::star2:")
+    st.markdown(
+        """
+        Defining the problem statement involves:
+
+        1. **Understanding the Use Case**: Align with real-world needs.
+        2. **Defining the Scope**: Set clear boundaries for the project.
+        3. **Identifying Key Metrics**: Quantify success with appropriate measures.
+        4. **Formulating the Problem**: Map it to a specific NLP task.
+
+        **Friendly Tip :bulb::**
+        A clear problem statement ensures your project stays focused, measurable, and achievable. Spend time here to avoid confusion later in the NLP pipeline!
+        """
+    )
+
+    st.divider()
+
+main()