# 📖 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")
```