🚀 Initial upload of my app

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo/demo.mp4 filter=lfs diff=lfs merge=lfs -text
+demo/demo.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Eslam Tarek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,20 +1,129 @@
+# license_plate_predicition — YOLOv8 License Plate Detection 📸🚀
+
+A lightweight Streamlit app that loads a trained YOLOv8 model and detects license plates in images with an intuitive chat-style UI.
+
+---
+
+## Badges
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)
+![Python](https://img.shields.io/badge/Python-3.10%2B-blue)
+
+---
+
+## Table of Contents
+
+- [Demo](#demo)
+- [Features](#features)
+- [Installation / Setup](#installation--setup)
+- [Usage](#usage)
+- [Configuration / Options](#configuration--options)
+- [Contributing](#contributing)
+- [License](#license)
+- [Acknowledgements / Credits](#acknowledgements--credits)
+
+---
+
+## Demo
+
+The repository includes real demo assets under `./demo/`.
+
+- Screenshot: `./demo/demo.png`
+
+  <img src="./demo/demo.png" alt="demo" width="640" />
+
+- Video: `./demo/demo.mp4`
+
+  <video src="./demo/demo.mp4" width="640" controls></video>
+
+---
+
+## Features
+
+- **YOLOv8 inference** via `ultralytics` for license plate detection.
+- **Chat-style Streamlit UI** to upload an image or paste an image URL.
+- **On-image annotations** with bounding boxes.
+- **Single-command launch** with Streamlit.
+
+---
+
+## Installation / Setup
+
+Use a virtual environment for isolation.
+
+```bash
+# Create a virtual environment
+python -m venv .venv
+
+# Activate it
+# On Linux/Mac:
+source .venv/bin/activate
+# On Windows:
+.venv\Scripts\activate
+
+# Upgrade pip (recommended)
+pip install --upgrade pip
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
 ---
-title: LPlateVision
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: YOLO-based License Plate Detection
-license: mit
+
+## Usage
+
+- Make sure you have a YOLOv8 model file at `./models/model.pt` (see [Configuration](#configuration--options)).
+- Launch the app:
+
+```bash
+streamlit run app.py
+```
+
+- In the app UI (`app.py` calling `render_chat_ui()` in `ui.py`):
+  - Choose "Upload Image" and provide a `.jpg/.jpeg/.png`, or
+  - Choose "Image URL" and paste a direct image URL.
+  - Click "Detect License Plate" to run inference. The result image with bounding boxes will be displayed.
+
+Programmatic highlights:
+
+- Model loading: `utils.load_model()` uses `ultralytics.YOLO` and Streamlit caching.
+- Inference: `utils.detect_license_plate()` runs `model.predict()` and returns an annotated image.
+
 ---
 
-# Welcome to Streamlit!
+## Configuration / Options
+
+- **Model path**: `app.py` loads `./models/model.pt` once at startup.
+  - Replace this file with your trained YOLOv8 weights.
+  - You can retrain with Ultralytics and export to `.pt`.
+- **Confidence threshold**: Set in `utils.detect_license_plate()` (default `conf=0.25`). Adjust as needed.
+- **Image inputs**:
+  - Upload via Streamlit file uploader.
+  - Provide a URL; the app fetches it using `requests` and decodes with OpenCV.
+
+---
+
+## Contributing
+
+Contributions are welcome! Please:
+
+1. Fork the repo.
+2. Create a feature branch.
+3. Commit changes with clear messages.
+4. Open a pull request describing the motivation and changes.
+
+For larger changes, consider opening an issue first to discuss the proposal.
+
+---
+
+## License
+
+This project is licensed under the MIT License. See [`LICENSE`](./LICENSE) for details.
+
+---
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+## Acknowledgements / Credits
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+- [`ultralytics`](https://github.com/ultralytics/ultralytics) — YOLOv8 model and inference utilities.
+- [`streamlit`](https://streamlit.io) — simple web app framework for ML demos.
+- [`opencv-python`](https://pypi.org/project/opencv-python/), [`numpy`](https://numpy.org/), and [`Pillow`](https://python-pillow.org/) for image handling and visualization.

app.py

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+import streamlit as st
+from ui import render_chat_ui
+from utils import load_model, detect_license_plate
+
+st.set_page_config(page_title="License Plate Detector", layout="centered")
+
+# Title
+st.title("📸 License Plate Detector")
+
+# Load YOLO model (runs once)
+model = load_model("./models/model.pt")
+
+# Chat-like interface
+render_chat_ui(model)

demo/demo.mp4

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models/model.pt

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requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+ultralytics==8.3.158
+streamlit==1.37.1
+numpy==1.26.4
+opencv-python==4.10.0.84
+requests==2.32.3
+Pillow==10.4.0
+pandas==2.2.2
+matplotlib==3.8.4

ui.py

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+import streamlit as st
+from utils import detect_license_plate, load_image_from_upload, load_image_from_url
+import io
+from PIL import Image
+import numpy as np
+
+def render_chat_ui(model):
+    st.markdown("Chat-style license plate detector. Upload an image or paste a URL.")
+
+    history = st.session_state.get("chat_history", [])
+    input_mode = st.radio("Choose Input Type", ["Upload Image", "Image URL"])
+
+    # Input
+    uploaded_file = None
+    image_url = None
+
+    if input_mode == "Upload Image":
+        uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    else:
+        image_url = st.text_input("Paste image URL")
+
+    submit = st.button("Detect License Plate")
+
+    if submit:
+        if input_mode == "Upload Image" and uploaded_file is not None:
+            image = load_image_from_upload(uploaded_file)
+            label = "User uploaded an image."
+        elif input_mode == "Image URL" and image_url.strip():
+            image = load_image_from_url(image_url)
+            label = f"User sent image URL: {image_url}"
+        else:
+            st.warning("Please provide a valid image.")
+            return
+
+        st.session_state.chat_history = history + [(label, image)]
+
+        # Detect and display
+        with st.spinner("Detecting license plate..."):
+            result_img = detect_license_plate(model, image)
+            st.image(result_img, caption="Bounding box", use_column_width=True)
+
+        # Save result to history
+        st.session_state.chat_history.append(("Bounding box", result_img))
+
+    

utils.py

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+from ultralytics import YOLO
+import numpy as np
+import cv2
+import tempfile
+import requests
+from PIL import Image
+import os
+import streamlit as st
+
+@st.cache_resource
+def load_model(model_path):
+    """Load the trained YOLOv8 model."""
+    return YOLO(model_path)
+
+def detect_license_plate(model, image: np.ndarray) -> np.ndarray:
+    """Detect license plate and return image with drawn bounding boxes."""
+    results = model.predict(image, conf=0.25)
+    annotated = results[0].plot()  # Draw boxes on the image
+    return annotated
+
+def load_image_from_upload(uploaded_file) -> np.ndarray:
+    """Load image from an uploaded file."""
+    image = Image.open(uploaded_file).convert("RGB")
+    return np.array(image)
+
+def load_image_from_url(url: str) -> np.ndarray:
+    """Download and convert an image from a URL."""
+    response = requests.get(url)
+    img_arr = np.asarray(bytearray(response.content), dtype=np.uint8)
+    image = cv2.imdecode(img_arr, cv2.IMREAD_COLOR)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image