refactor: Implemented professional modular structure (apds package)

README.md

@@ -45,12 +45,24 @@ An AI-powered Road Damage Assessment system using YOLOv11s & SAHI to detect poth
    Ensure your `.pt` files are placed in the `models/` directory structure:
    - `models/crack_detector_v12/best.pt`
    - `models/pothole_v1/best.pt`
+   - `models/pothole_v2/best.pt` (Experiment V2)
 
 5. **Run the App**:
    ```bash
    streamlit run app.py
    ```
 
+## 📂 Project Structure
+
+Following high-level professional standards, this project is organized as a modular package:
+
+- **`app.py`**: The main entry point for the Streamlit application.
+- **`apds/`**: The core package containing the detection engine, geospatial logic, and AI consultant bridge.
+- **`models/`**: Centralized storage for trained weights and performace validation artifacts.
+- **`docs/`**: Indian Road Congress (IRC) engineering manuals used by the RAG system.
+- **`tests/`**: Unit testing suite for core components.
+- **`scripts/`**: Development and historical training scripts.
+
 ## 📊 Methodology
 
 This project utilizes **SAHI** to overcome the limitations of standard object detectors on small, narrow distresses like cracks. By tiling images during inference, we maintain resolution and capture the fine textures required for accurate road distress classification.

path_utils.py → apds/path_utils.py

@@ -7,7 +7,7 @@ Centralised path handling for the Pothole Detection Streamlit app.
 from pathlib import Path
 
 # ── Root of the project ────────────────────────────────────────────────────────
-ROOT = Path(__file__).resolve().parent
+ROOT = Path(__file__).resolve().parent.parent
 
 def get_available_models() -> dict:
     """

rag_utils.py → apds/rag_utils.py

@@ -14,8 +14,8 @@ from langchain.chains import RetrievalQA
 
 # ── Configuration ───────────────────────────────────────────────────────────
 
-DB_PATH = Path("models/vector_db")
-KNOWLEDGE_DIR = Path("Knowledge_base")
+DB_PATH = Path(__file__).resolve().parent.parent / "models" / "vector_db"
+KNOWLEDGE_DIR = Path(__file__).resolve().parent.parent / "docs"
 
 @st.cache_resource
 def get_embeddings():

app.py

@@ -117,5 +117,5 @@ st.markdown(
 )
 
 # ── Import and render dashboard ───────────────────────────────────────────────
-import dashboard
+from apds import dashboard
 dashboard.render()

docs/irc.gov.in.082.2015.pdf

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+size 988736

docs/irc.gov.in.sp.020.2002.pdf

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docs/mort.250.2013.pdf

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