Delete pages_Pipeline.py

pages_Pipeline.py

@@ -1,58 +0,0 @@
-import streamlit as st
-
-st.markdown(
-    """
-    <div style="text-align: center;">
-        <h1><b>Natural Language Processing Pipeline</b></h1>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
-
-st.image("1726065094370.png",use_container_width = True)
-
-st.subheader("NLP Pipeline Steps")
-
-st.write("""
-1. **Text Input and Data Collection**  
-   - **What it is**: Collecting the text data to be analyzed or processed.  
-   - **Sources**: Websites, documents, emails, social media, etc.  
-   - **Why it’s important**: Provides the raw data necessary to build an NLP system.
-
-2. **Text Preprocessing**  
-   - **What it is**: Cleaning and preparing the raw text for analysis.  
-   - **Examples**:  
-     - Removing punctuation, numbers, and stopwords.  
-     - Lowercasing text, tokenization, and stemming.  
-   - **Why it’s important**: Ensures that the data is clean and structured for better results.
-
-3. **Text Representation**  
-   - **What it is**: Converting text into a numerical format that a machine can understand.  
-   - **Examples**: Techniques like Bag of Words, TF-IDF, or word embeddings (Word2Vec, BERT).  
-   - **Why it’s important**: Machines work with numbers, not raw text, so this step is essential.
-
-4. **Feature Selection**  
-   - **What it is**: Selecting the most relevant pieces of data or words for the task.  
-   - **Examples**: Choosing keywords or focusing on specific phrases that matter for classification or prediction.  
-   - **Why it’s important**: Reduces noise in the data and improves the model’s performance.
-
-5. **Model Selection and Training**  
-   - **What it is**: Choosing an appropriate machine learning or deep learning model and training it on the data.  
-   - **Examples**: Algorithms like Logistic Regression, SVM, or deep learning models like BERT.  
-   - **Why it’s important**: The model learns patterns in the data to perform tasks like classification or translation.
-
-6. **Model Deployment and Inference**  
-   - **What it is**: Deploying the trained model to a real-world environment to make predictions or analyze text.  
-   - **Examples**: A chatbot responding to queries or a search engine ranking results.  
-   - **Why it’s important**: Makes the model usable for solving real-world problems.
-
-7. **Evaluation and Optimization**  
-   - **What it is**: Assessing the model’s performance and fine-tuning it for better results.  
-   - **Examples**: Using metrics like accuracy, precision, recall, or F1-score to evaluate the model.  
-   - **Why it’s important**: Ensures the model is reliable and effective in its task.
-
-8. **Iteration and Improvements**  
-   - **What it is**: Continuously updating and improving the model based on new data or feedback.  
-   - **Examples**: Retraining the model when new data is available or tweaking features to improve performance.  
-   - **Why it’s important**: Keeps the system relevant and accurate over time.
-""")