Refactor: Integrate backend API and normalize data

This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage.

Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>

backend/plan.md

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+Perfect 👍 Before I drop a full codebase, let’s agree on the **flow + plan** so you (and your team) know exactly how things connect. This will also make sure the code I give matches your hackathon goals.
+
+---
+
+# ⚡ Backend Flow (FixMate Local Prototype)
+
+### 1. Citizen Upload Flow
+
+1. **Citizen uses frontend** (simple React, Streamlit, or Swagger UI for now).
+2. Submits:
+
+   * Photo (issue picture).
+   * GPS location (lat/lng) (either auto from frontend or manually typed for demo).
+   * Optional notes.
+3. Backend endpoint: `POST /report`
+
+   * Saves photo → `./app/static/uploads/`
+   * Runs **AI classification** (via YOLOv8 model from Hugging Face).
+   * Runs **severity logic** (based on bounding box size / confidence).
+   * Generates **ticket record** in DB.
+   * Returns JSON: `{id, category, severity, status, description}`
+
+---
+
+### 2. AI Model Flow
+
+* First time backend runs:
+
+  * Check if `models/` folder exists. If not, create it.
+  * Use **`hf_hub_download`** to fetch YOLOv8n weights into `./models/`.
+  * Load the model from that path with `ultralytics.YOLO`.
+* Every report:
+
+  * Pass image to model → detect objects.
+  * Map objects to FixMate categories (`pothole`, `streetlight`, `trash`, `signage`).
+  * Apply **severity scoring** (e.g. bounding box area = High if > certain %).
+* If model fails (no internet, missing weights):
+
+  * Use fallback heuristic (OpenCV contour/brightness detection).
+
+---
+
+### 3. Ticket Lifecycle Flow
+
+* Ticket schema:
+
+  ```
+  id, image_path, category, severity, location, description, status, timestamps
+  ```
+* Default status = `"New"`.
+* Admin dashboard endpoints:
+
+  * `GET /tickets` → list all tickets.
+  * `GET /tickets/{id}` → fetch ticket details.
+  * `PATCH /tickets/{id}` → update status (`In Progress`, `Fixed`).
+* Citizens can query:
+
+  * `GET /status/{id}` → see ticket’s status.
+
+---
+
+### 4. Dashboard & Analytics Flow
+
+* Admin UI (or Swagger demo) calls:
+
+  * `/tickets` → display list or map markers.
+  * `/analytics` → simple stats:
+
+    * Total tickets.
+    * Counts by category & severity.
+    * (Optional) Location clustering for hotspots.
+
+---
+
+# 🛠️ Development Plan
+
+### Step 1 – Environment & Repo
+
+* Conda or venv, install dependencies (FastAPI, SQLAlchemy, ultralytics, huggingface\_hub).
+* Initialize Git repo with `.gitignore`, `requirements.txt`.
+
+### Step 2 – Database & Models
+
+* SQLite with SQLAlchemy ORM.
+* `Ticket` model with enum fields for severity + status.
+
+### Step 3 – AI Service
+
+* `ai_service.py` handles:
+
+  * Ensure `models/` exists.
+  * Download YOLOv8 from Hugging Face into `./models/`.
+  * Load model.
+  * `detect_issue(image_path)` returns `{category, severity, confidence}`.
+
+### Step 4 – Ticket Service
+
+* Saves image locally.
+* Calls `ai_service.detect_issue()`.
+* Creates DB record.
+
+### Step 5 – API Routes
+
+* `/report` → citizen upload.
+* `/tickets` → list all tickets.
+* `/tickets/{id}` → fetch ticket.
+* `/tickets/{id}` PATCH → update status.
+* `/analytics` → summary stats.
+
+### Step 6 – Demo Prep
+
+* Populate DB with some sample tickets.
+* Upload a few pothole/streetlight images → verify classification.
+* Test via Swagger UI at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs).
+
+---
+
+✅ With this flow, you’ll have a **complete hackathon backend** that:
+
+* Works offline after first model download.
+* Saves everything locally (SQLite + images).
+* Provides APIs ready for a frontend dashboard.
+
+---
+
+👉 Do you want me to now **rewrite the backend code** with this Hugging Face + `models/` folder integration (full project files), so you can just copy-paste into your repo and run?