README.md

---
license: mit
language:
  - en
tags:
  - intent-classification
  - emotion-detection
  - mental-health
  - lstm
  - sentence-transformers
  - sklearn
pipeline_tag: text-classification
---

# 🧠 MindPadi: Hybrid Classifier Suite

This repository contains auxiliary models for intent and emotion classification used in the **MindPadi** mental health assistant. These models include rule-based, ML-based, and deep learning classifiers trained to detect emotional states, user intent, and conversational cues.


## 📦 Files

| File                          | Description                                              |
|-------------------------------|----------------------------------------------------------|
| `intent_clf.joblib`           | scikit-learn pipeline for intent classification (TF-IDF) |
| `intent_sentence_classifier.pkl` | Sentence-level intent classifier (pickle)                |
| `lstm_tfidf.h5`               | LSTM model trained on TF-IDF vectors                    |
| `lstm_bert.h5`                | LSTM model trained on BERT embeddings                   |
| `tfidf_vectorizer.pkl`        | TF-IDF vectorizer for preprocessing text                |
| `tfidf_embeddings.pkl`        | Cached TF-IDF embeddings for faster lookup              |
| `bert_embeddings.npy`         | Precomputed BERT embeddings used in training/testing     |
| `lstm_accuracy_tfidf.png`     | Evaluation plot (TF-IDF model)                          |
| `lstm_accuracy_bert.png`      | Evaluation plot (BERT model)                            |
| `model_configs/`              | JSON configs for training and architecture              |


## 🎯 Tasks Supported

- **Intent Classification**: Understand what the user is trying to communicate.
- **Emotion Detection**: Identify the emotional tone (e.g., sad, angry).
- **Embedding Generation**: Support vector similarity or hybrid routing.



## 🔬 Model Overview

| Model Type     | Framework   | Notes                                 |
|----------------|-------------|----------------------------------------|
| LSTM + TF-IDF  | Keras       | Traditional pipeline with good generalization |
| LSTM + BERT    | Keras       | Handles contextual sentence meanings  |
| TF-IDF + SVM   | scikit-learn | Lightweight and interpretable intent routing |
| Sentence Classifier | scikit-learn | Quick rule or decision-tree model for sentence-level labels |

---

## 🛠️ Usage Example

### Intent Prediction (Joblib)

```python
from joblib import load

clf = load("intent_clf.joblib")
text = ["I feel really anxious today"]
pred = clf.predict(text)

print("Intent:", pred[0])
````

### LSTM Emotion Prediction

```python
from tensorflow.keras.models import load_model
import numpy as np

model = load_model("lstm_bert.h5")
embeddings = np.load("bert_embeddings.npy")  # assuming aligned with test set
output = model.predict(embeddings)

print("Predicted emotion class:", output.argmax(axis=1))
```



## 📊 Evaluation

| Model               | Accuracy | Dataset Size | Notes                            |
| ------------------- | -------- | ------------ | -------------------------------- |
| `lstm_bert.h5`      | \~88%    | 10,000+      | Best for nuanced emotional input |
| `lstm_tfidf.h5`     | \~83%    | 10,000+      | Lighter, faster                  |
| `intent_clf.joblib` | \~90%    | 8,000+       | Works well with short queries    |

Evaluation visualizations:

* ![](lstm_accuracy_bert.png)
* ![](lstm_accuracy_tfidf.png)



## ⚠️ Limitations

* English only
* May misclassify ambiguous or sarcastic phrases
* LSTM models require matching vectorizer or embeddings



## 🧩 Integration

These models are invoked in:

* `app/chatbot/intent_classifier.py`
* `app/chatbot/emotion.py`
* `app/utils/embedding_search.py`



## 🧠 Intended Use

* Mental health journaling feedback
* Chatbot-based emotion understanding
* Offline fallback for heavy transformer models



## 📄 License

MIT License – free for commercial and research use.

*Last updated: May 6, 2025*