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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—now including the actual code inline so even a beginner can follow! | |
| --- | |
| ## 📝 Overview | |
| - **Purpose** | |
| Classify text as **Positive** or **Negative**, one sentence at a time or in bulk via CSV/XLSX upload, and visualize the distribution in a pie chart. | |
| - **Tech Stack** | |
| - **Model**: A DistilBERT sentiment model fine-tuned on SST-2 | |
| - **UI**: Gradio Blocks + Tabs | |
| - **Data I/O**: Pandas for CSV/XLSX | |
| - **Visualization**: Matplotlib pie chart | |
| --- | |
| ## ⚙️ Setup & Dependencies | |
| Install the required libraries: | |
| `pip install transformers gradio pandas matplotlib torch` | |
| --- | |
| ## 🔍 Block-by-Block Explanation | |
| ### 1. Imports | |
| Use the following imports at the top of your script: | |
| - `from transformers import pipeline` | |
| - `import gradio as gr` | |
| - `import pandas as pd` | |
| - `import matplotlib.pyplot as plt` | |
| ### 2. Model Loading | |
| Initialize the sentiment-analysis pipeline with DistilBERT fine-tuned on SST-2: | |
| - sentiment_analyzer = pipeline( | |
| "sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| ### 3. Single-Text Analysis | |
| Define a function to predict sentiment on one string: | |
| - `def analyze_sentiment(text):` | |
| `result = sentiment_analyzer(text)` | |
| `return result[0]['label']` | |
| ### 4. File-Upload Analysis | |
| Define a function to read a CSV/XLSX, predict each row, and build a pie chart: | |
| - `def analyze_file(file):` | |
| - `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` | |
| - `if "Text" not in df.columns:` | |
| - `return "Missing 'Text' column", None` | |
| - `sentiments = [sentiment_analyzer(t)[0]['label'] for t in df["Text"].fillna("")]` | |
| - `df_result = pd.DataFrame({"Text": df["Text"], "Sentiment": sentiments})` | |
| - `counts = df_result["Sentiment"].value_counts()` | |
| - `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` | |
| ### 5. Gradio UI | |
| Assemble the UI with two tabs: | |
| - `with gr.Blocks() as demo:` | |
| - `gr.Markdown("## 🧠 Text Sentiment Analyzer")` | |
| - `gr.Markdown("Type text or upload a CSV/XLSX with a **Text** column.")` | |
| - **Single Text Tab** | |
| - `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)` | |
| - **Upload File Tab** | |
| - `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])` | |
| ### 6. Launching the App | |
| Call `demo.launch()` to start the local web interface. | |
| --- | |
| ## 🚀 Core Concepts | |
| | Concept | Why It Matters | | |
| |---------------------------|-------------------------------------------------------| | |
| | **Hugging Face Pipeline** | Loads and runs a pre-trained model in one line | | |
| | **DataFrame I/O** | Bulk processing of CSV & Excel data | | |
| | **Matplotlib Pie** | Visual summary of classification breakdown | | |
| | **Gradio Blocks** | Build interactive web apps with pure Python | | |
| --- | |
| ## 🔄 Swapping Models & Extensions | |
| - **Neutral Sentiment** | |
| Use a three-label model: | |
| `pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")` | |
| - **Multilingual Support** | |
| Use a star-rating model: | |
| `pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")` | |
| - **Emotion Detection** | |
| Choose an emotion-classification model: | |
| `pipeline("sentiment-analysis", model="bhadresh-savani/bert-base-uncased-emotion")` | |