code_explanation.md

📖 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!

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📝 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

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⚙️ Setup & Dependencies

Install the required libraries:
pip install transformers gradio pandas matplotlib torch

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🔍 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.

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🚀 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

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🔄 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")