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| # 📖 Code Explanation: Text Sentiment Analyzer | |
| This document walks you through the **Text Sentiment Analyzer** (with file upload & pie-chart) step by step—fully in Markdown, with detailed inline code explanations. | |
| --- | |
| ## 📝 Overview | |
| **Purpose** | |
| Classify text as **Positive** or **Negative**, individually or in bulk via CSV/XLSX files, visualizing results in a pie chart. | |
| **Tech Stack** | |
| - **Model**: DistilBERT fine-tuned on SST-2 dataset | |
| - **UI**: Gradio Blocks + Tabs | |
| - **Data Handling**: Pandas for CSV/XLSX operations | |
| - **Visualization**: Matplotlib for pie chart visualization | |
| --- | |
| ## ⚙️ Setup & Dependencies | |
| Install the required libraries: | |
| ``` | |
| pip install transformers gradio pandas matplotlib torch | |
| ``` | |
| --- | |
| ## 🔍 Detailed Block-by-Block Code Explanation | |
| ### 1. Imports | |
| ``` | |
| from transformers import pipeline | |
| import gradio as gr | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| ``` | |
| **Detailed Explanation:** | |
| - `pipeline` from Transformers: Loads and runs pre-trained NLP models easily. | |
| - `gradio`: Allows creation of web-based interfaces quickly from Python. | |
| - `pandas`: Reads CSV/Excel files into DataFrame objects and manages tabular data. | |
| - `matplotlib`: Enables visualization of data in graphical form. | |
| ### 2. Loading the Sentiment Analysis Model | |
| ``` | |
| sentiment_analyzer = pipeline("sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english") | |
| ``` | |
| **Detailed Explanation:** | |
| - Initializes a pre-trained DistilBERT model fine-tuned specifically for sentiment analysis. | |
| - The chosen model can classify text into two sentiment categories: `"POSITIVE"` or `"NEGATIVE"`. | |
| ### 3. Function for Single-Text Analysis | |
| ``` | |
| def analyze_sentiment(text): | |
| result = sentiment_analyzer(text) | |
| return result[0]['label'] | |
| ``` | |
| **Detailed Explanation:** | |
| - Receives a single text input as parameter. | |
| - Passes the text to the loaded sentiment analyzer model. | |
| - Retrieves and returns only the sentiment label (Positive or Negative). | |
| ### 4. Function for Bulk Analysis via File Upload | |
| ``` | |
| def analyze_file(file): | |
| # Determine file format (CSV or Excel) and read into a DataFrame | |
| if file.name.endswith(".csv"): | |
| df = pd.read_csv(file.name) | |
| elif file.name.endswith(".xlsx"): | |
| df = pd.read_excel(file.name) | |
| else: | |
| return "Unsupported file format", None | |
| # Check if required 'Text' column exists in the uploaded file | |
| if "Text" not in df.columns: | |
| return "Missing 'Text' column", None | |
| # Apply sentiment analysis model to each text row | |
| sentiments = [sentiment_analyzer(t)[0]['label'] for t in df["Text"].fillna("")] | |
| # Create a results DataFrame with original texts and their sentiments | |
| df_result = pd.DataFrame({"Text": df["Text"], "Sentiment": sentiments}) | |
| # Count sentiment occurrences (Positive/Negative) | |
| counts = df_result["Sentiment"].value_counts() | |
| # Generate a pie chart to visualize sentiment distribution | |
| fig, ax = plt.subplots() | |
| ax.pie(counts.values, labels=counts.index, autopct="%1.1f%%", | |
| colors=["#66bb6a", "#ef5350"][:len(counts)]) | |
| ax.set_title("Sentiment Distribution") | |
| return df_result, fig | |
| ``` | |
| **Detailed Explanation:** | |
| - Loads data from CSV or Excel based on file extension. | |
| - Checks for existence of the necessary column (`Text`). | |
| - Predicts sentiment for each entry and compiles results into a table. | |
| - Visualizes sentiment results with a pie chart for intuitive interpretation. | |
| ### 5. Building the Gradio Web UI | |
| ``` | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🧠 Text Sentiment Analyzer") | |
| gr.Markdown("Type text or upload a CSV/XLSX with a **Text** column.") | |
| # Tab for analyzing single text entries | |
| with gr.Tab("🔤 Single Text"): | |
| txt = gr.Textbox(label="Enter text", lines=4) | |
| out = gr.Textbox(label="Sentiment") | |
| btn = gr.Button("Analyze") | |
| btn.click(analyze_sentiment, inputs=txt, outputs=out) | |
| # Tab for bulk file upload and analysis | |
| with gr.Tab("📁 Upload File"): | |
| file_in = gr.File(label="Upload CSV or Excel") | |
| table_out = gr.Dataframe(label="Results") | |
| chart_out = gr.Plot(label="Pie Chart") | |
| btn2 = gr.Button("Analyze File") | |
| btn2.click(analyze_file, inputs=file_in, outputs=[table_out, chart_out]) | |
| # Launch the web application | |
| demo.launch() | |
| ``` | |
| **Detailed Explanation:** | |
| - Constructs a user-friendly interface with Gradio Blocks. | |
| - Provides two distinct tabs for different analysis modes: single text and bulk upload. | |
| - Links buttons to their corresponding analysis functions, clearly defining input and output elements. | |
| --- | |
| ## 🔄 Model Swaps and Further Enhancements | |
| - **Neutral Sentiment** (3-class sentiment analysis): | |
| ``` | |
| pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment") | |
| ``` | |
| - **Multilingual Support** (1-5 Star Ratings): | |
| ``` | |
| pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment") | |
| ``` | |
| - **Emotion Detection** (Classifying emotions): | |
| ``` | |
| pipeline("sentiment-analysis", model="bhadresh-savani/bert-base-uncased-emotion") | |
| ``` | |