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import streamlit as st
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()
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