test

Dockerfile

@@ -15,7 +15,7 @@ COPY . /app
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Make port available to the world outside this container
-EXPOSE 8001
+EXPOSE 7860
 
 # Run your FastAPI app
 CMD ["python", "api.py"]

README.md

@@ -1,80 +1,8 @@
-# Project Name
-🌟Image Object Detection🌟
-
-## Description
-This is a FastAPI-based web service that accepts an image upload and performs object detection on the image using a pre-trained model Yolov5m. It returns the annotated image with the detected objects.
-
-## Table of Contents
-- [Installation](#installation)
-- [Features](#features)
-- [Usage](#usage)
-- [Configuration](#configuration)
-- [Contributing](#contributing)
-- [License](#license)
-
-## Installation
-1. Clone the repository: 
-```bash
-git clone https://github.com/Codelinhtinh/Exercises-AIO.git
-```
-2. Install project dependencies:
-```bash
-pip install -r requirements.txt
-```
-## Features
-✨This code is a FastAPI application that provides an API endpoint for performing predictions on uploaded images.
-
-✨ It uses a pre-trained model to detect objects in images. 
-
-✨ Uploaded images are processed and resized to a specific image size defined in the CFG class. 
-
-✨ The final results are visualized on the original image using the visualize function.
-
-✨The processed and visualized image is saved to a temporary file and returned as a response to the API request.
-## Usage
-1. Run the FastAPI server in makefile:
-```bash
-uvicorn api:api --port 8000
-```
-
-2. Open your browser and navigate to `http://localhost:8000/docs` to check prediction.
-![Alt Text](sample\api.gif)
-
-3. Start the streamlit app in makefile:
-```bash
-streamlit run app.py
-```
-4. Import image and wait:
-![Alt Text](sample\Usage.gif)
-
-## Configuration
-You can modify the configuration parameters of the model by updating the CFG class in the configs.py file. The available configuration options are:
-
-**image_size**: The size of the input image for prediction.
-
-**conf_thres**: The confidence threshold for object detection.
-
-**iou_thres**: The IoU (Intersection over Union) threshold for non-maximum suppression.
-
-Feel free to adjust these parameters according to your requirements.
-
-
-## Contributing
-🤝 Contributions are welcome! Please follow these guidelines when contributing to the project:
-1. Fork the repository.
-2. Create a new branch: 
-```bash
-git checkout -b feature/your-feature
-```
-3. Commit your changes: 
-```bash
-git commit -m 'Add your feature'
-```
-4. Push to the branch: 
-```bash
-git push origin feature/your-feature
-```
-5. Create a pull request.
-
-## License
-📝 This project is open source
+---
+title: Basic Docker SDK Space
+emoji: 🐳
+colorFrom: purple
+colorTo: gray
+sdk: docker
+app_port: 7860
+---

api.py

@@ -51,4 +51,4 @@ async def predict(file: UploadFile):
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(api, host="0.0.0.0", port=8001)
+    uvicorn.run(api, host="0.0.0.0", port=7860)

docker-compose.yml

@@ -5,12 +5,12 @@ services:
       context: .
       dockerfile: Dockerfile
     ports:
-      - "8001:8001"
+      - "7860:7860"
     command: 
       - "uvicorn"
       - "api:api"
       - "--reload"
-      - "--port=8001"
+      - "--port=7860"
       - "--host=0.0.0.0"
     volumes:
       - .:/app  # Replace with the path to your local code