| --- |
| title: Complaint Auto-Routing System |
| colorFrom: blue |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 5.44.0 |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # Complaint Auto-Routing System |
|
|
| An end-to-end machine learning system that processes citizen complaints submitted in text, audio, or video format. It is designed to run fully offline without external APIs. |
|
|
| It automatically performs the following tasks: |
| - **Officer Routing**: Assigns the complaint to the most suitable officer from an internal list using an SVM. |
| - **Priority Prediction**: Classifies the priority as High, Medium, or Low using a Random Forest. |
| - **ETA Prediction**: Estimates resolution time in days using a Gradient Boosting Regressor. |
| - **Similarity Search**: Retrieves similar past complaints using cosine similarity over text embeddings. |
|
|
| --- |
| ## Project Screenshots |
| # Text Complaint |
| <img width="1001" height="1279" alt="image" src="https://github.com/user-attachments/assets/681e20c1-a829-4f33-812e-c4296e170ece" /> |
|
|
| # Audio Complaint |
| <img width="992" height="1274" alt="image" src="https://github.com/user-attachments/assets/02e6677f-0c7c-4c49-9996-c5dcac5783f3" /> |
|
|
| # Video Complaint |
| <img width="984" height="1289" alt="image" src="https://github.com/user-attachments/assets/8cdc0fef-3699-4c48-bee3-84f76c2e9fb7" /> |
|
|
|
|
| ## Project Structure |
|
|
| ``` |
| complaint-routing-system/ |
| ├── data/ |
| │ ├── generate_data.py # Synthetic complaint generator |
| │ └── synthetic_complaints.csv # 800 labelled complaints (auto-generated) |
| ├── models/ |
| │ ├── train.py # End-to-end training pipeline |
| │ └── saved/ # Trained model artifacts |
| ├── inference/ |
| │ ├── embedding_engine.py # TF-IDF+SVD or sentence-transformers |
| │ ├── vector_store.py # NumPy cosine search |
| │ └── engine.py # Core inference engine (load + predict) |
| ├── audio_video/ |
| │ └── transcriber.py # Whisper-based offline ASR (audio + video) |
| ├── app/ |
| │ ├── cli.py # Command-line interface |
| │ └── web_app.py # Gradio web UI |
| ├── evaluation/ |
| │ └── evaluate.py # Full evaluation suite |
| ├── requirements.txt |
| └── README.md |
| ``` |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ### 1. Install dependencies |
|
|
| ```bash |
| # Core requirements |
| pip install scikit-learn numpy pandas scipy joblib |
| |
| # Embedding model |
| pip install sentence-transformers |
| |
| # Web UI |
| pip install gradio |
| |
| # Audio/Video transcription (requires ffmpeg installed on your system) |
| pip install openai-whisper |
| ``` |
|
|
| ### 2. Generate training data and train models |
|
|
| ```bash |
| python data/generate_data.py |
| python models/train.py |
| ``` |
|
|
| ### 3. Run inference |
|
|
| ```bash |
| # Interactive CLI |
| python app/cli.py |
| |
| # Direct text |
| python app/cli.py --text "Pothole on MG Road near hospital causing accidents. URGENT!" |
| |
| # Web UI |
| python app/web_app.py |
| ``` |
|
|
| ### 4. Run evaluation |
|
|
| ```bash |
| python evaluation/evaluate.py |
| ``` |
|
|
| --- |
|
|
| ## Architecture |
|
|
| The system takes in text, audio, or video. If audio or video is provided, it uses the local Whisper model to transcribe the speech offline. |
|
|
| The text is then passed to an embedding engine (using sentence-transformers). The resulting vector is passed to three separate scikit-learn models: |
| 1. Support Vector Machine (Officer Routing) |
| 2. Random Forest (Priority) |
| 3. Gradient Boosting Regressor (ETA) |
|
|
| The vector is also compared against a local NumPy store of historical complaints using cosine similarity to fetch the most relevant past issues. |
|
|
| --- |
|
|
| ## Evaluation Results (5-fold Cross-Validation) |
|
|
| ### Officer Routing (SVM) |
| | Metric | Value | |
| |--------|-------| |
| | CV Accuracy | 1.000 ± 0.000 | |
| | CV F1-macro | 1.000 ± 0.000 | |
|
|
| Note: The synthetic data has very clear departmental boundaries (e.g., "pothole" maps strictly to roads). In a real-world scenario, accuracy would be closer to 85-90%. |
|
|
| ### Priority Prediction (Random Forest) |
| | Metric | Value | |
| |--------|-------| |
| | CV Accuracy | 0.578 ± 0.014 | |
| | CV F1-macro | 0.566 ± 0.020 | |
|
|
| ### ETA Prediction (Gradient Boosting) |
| | Metric | Value | |
| |--------|-------| |
| | CV MAE | 5.25 days | |
| | CV RMSE | 7.62 days | |
|
|
| --- |
|
|
| ## Replacing Synthetic Data with Real Data |
|
|
| 1. Prepare a CSV with columns: `text`, `officer_id`, `priority`, `eta_days` |
| 2. Replace `data/synthetic_complaints.csv` |
| 3. Run `python models/train.py` |
|
|
| The pipeline will automatically retrain all models on the new data and overwrite the old artifacts in `models/saved`. |
|
|
