🎭 Emotion AI Classifier

A real-time web application that classifies text into six emotions using TF-IDF and Logistic Regression. Built with Streamlit, the app provides predictions, confidence scores, and performance metrics.

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📚 Dataset Used

The dataset includes 3 CSV files:

• training.csv
• test.csv
• validation.csv

Each file contains:

• text: the user’s message
• label: an integer from 0 to 5 representing emotion

Emotion Mapping:

Label	Emotion
0	😢 Sadness
1	😊 Joy
2	❤️ Love
3	😠 Anger
4	😨 Fear
5	😲 Surprise

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🧠 Approach Summary

1. Preprocessing:

   • Lowercasing
   • Punctuation removal
   • Extra whitespace cleanup

2. Vectorization:

   • TF-IDF with 5000 features
   • Removes English stopwords

3. Model:

   • LogisticRegression(max_iter=1000) from scikit-learn
   • Trained on the training.csv set
   • Evaluated on the test.csv set

4. Evaluation Metrics:

   • Accuracy score
   • Classification report
   • Confusion matrix (visualized via Plotly)

5. Streamlit UI:

   • Styled layout
   • Real-time predictions
   • Example sentence testing
   • Confidence bar display

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🔧 Dependencies

Install using pip:

pip install -r requirements.txt

Run