Update README.md

README.md

@@ -1,80 +1,131 @@
-Here's a `README.md` for your project:
+---
+license: gpl-3.0
+title: VIDEO MANIPULATION DETECTION
+sdk: streamlit
+---
+For deploying your project on **Streamlit** in a Space, here's the adjusted **README.md** with relevant details:
 
 ```markdown
-# Video Manipulation Detection Using AI
+# Video Manipulation Detection - Streamlit App
+
+## Overview
+This project is an AI-powered tool designed to detect video manipulations like deepfakes using object detection and optical flow analysis. It provides a web interface where users can upload videos, and the tool analyzes the video for potential manipulations, giving a score to indicate the likelihood of manipulation.
+
+## Configuration
+
+```yaml
+title: Video Manipulation Detection
+emoji: 🎥
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+python_version: 3.10
+sdk_version: 1.10.0
+suggested_hardware: "t4-medium"
+suggested_storage: "medium"
+app_file: app.py
+app_port: 8501
+base_path: /
+fullWidth: true
+header: default
+short_description: Detect deepfakes and video manipulations using AI-based analysis.
+models:
+  - facebook/detr-resnet-50
+tags:
+  - deepfake-detection
+  - video-analysis
+  - AI
+thumbnail: https://example.com/thumbnail.png
+pinned: true
+hf_oauth: false
+hf_oauth_scopes: []
+hf_oauth_expiration_minutes: 480
+hf_oauth_authorized_org: []
+disable_embedding: false
+startup_duration_timeout: 30m
+custom_headers: {}
+preload_from_hub:
+  - facebook/detr-resnet-50
+```
 
-This project uses an AI-powered video analysis pipeline to detect potential video manipulations such as deepfakes and other visual tampering. It leverages the **DEtection TRansformer (DETR)** model for object detection and optical flow analysis to identify motion inconsistencies. The application is built using **Streamlit**, and the model processes videos to generate detailed reports on their authenticity.
+## Description
 
-## Features
-- **Metadata Extraction**: Extracts and displays metadata from uploaded videos.
-- **Frame Extraction**: Extracts frames from videos at specified intervals for analysis.
-- **Object Detection**: Analyzes each frame to detect objects using the DETR model.
-- **Optical Flow Calculation**: Computes motion patterns to detect inconsistencies.
-- **Report Generation**: Compiles visual and motion analysis results into a comprehensive JSON report.
-- **Manipulation Detection**: Uses the report to assess the likelihood of video manipulation.
+This Streamlit app allows users to upload a video, which is then analyzed for potential manipulation. It uses the following key features:
 
-## Technologies Used
-- **Streamlit**: Front-end web framework for creating the user interface.
-- **PyTorch**: Machine learning framework used for object detection (DETR).
-- **OpenCV**: Library for video and image processing (frame extraction, optical flow).
-- **Transformers**: Pre-trained DETR model for object detection.
-- **FFmpeg**: Tool for extracting metadata from videos and processing video files.
+1. **Frame Extraction**: Captures frames from the video at defined intervals.
+2. **Object Detection**: Uses the **DETR model** (a transformer-based object detection model) to detect objects in each frame.
+3. **Optical Flow Calculation**: Computes motion patterns between consecutive frames to detect any abnormal motion (indicative of manipulation).
+4. **Manipulation Score**: Based on the results of object detection and motion analysis, the app generates a manipulation score to indicate the likelihood of tampering.
 
-## Installation
+## Steps to Deploy
 
-1. Clone the repository:
+1. **Clone the Repository**:
    ```bash
-   git clone https://github.com/your-username/video-manipulation-detection.git
+   git clone https://github.com/yourusername/video-manipulation-detection.git
    cd video-manipulation-detection
    ```
 
-2. Install dependencies:
+2. **Create a Virtual Environment**:
+   Create a virtual environment (optional but recommended):
+   ```bash
+   python3 -m venv venv
+   ```
+   Activate the virtual environment:
+   - On Windows: `venv\Scripts\activate`
+   - On Mac/Linux: `source venv/bin/activate`
+
+3. **Install Dependencies**:
+   Install the required dependencies:
    ```bash
    pip install -r requirements.txt
    ```
 
-3. Run the Streamlit app:
+4. **Run the Streamlit App**:
+   To run the app locally:
    ```bash
    streamlit run app.py
    ```
+   This will start the app on `http://localhost:8501`.
 
-## How to Use
+5. **Deployment on Streamlit Space**:
+   - Push the repository to **Hugging Face Spaces** under your account.
+   - Ensure the **`app.py`** file is located at the root of the repository.
+   - The app will automatically run in the cloud on Streamlit.
 
-1. **Upload a Video**: Go to the "Analyze Video" section and upload a `.mp4` or `.mov` video file.
-2. **Start Analysis**: Click the **"Analyze Video"** button to start processing.
-3. **View Results**: After analysis, the app will display a score and details about the potential manipulation of the video.
+## Requirements
 
-## Report Details
-The report includes the following information:
-- **Frame Analysis**: Number of detections, maximum and average confidence of the object detection model.
-- **Motion Analysis**: Calculated optical flow values to detect motion inconsistencies.
-- **Summary Stats**: Overall detection confidence, optical flow statistics, and manipulation detection score.
+- Python 3.10
+- Streamlit
+- OpenCV
+- PyTorch
+- Hugging Face Transformers
+- PIL (Python Imaging Library)
+- tqdm
+
+## Usage
+
+1. Upload a video file (e.g., `.mp4`, `.mov`).
+2. The app will process the video and display:
+   - The **manipulation score** based on the likelihood of manipulation.
+   - **Frame-based analysis** and **optical flow** insights.
+3. View the final result on the app interface, indicating whether the video is likely manipulated.
 
 ## License
+
 This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
 
 ## Acknowledgments
-- [Streamlit](https://streamlit.io/)
-- [PyTorch](https://pytorch.org/)
-- [Hugging Face Transformers](https://huggingface.co/)
-- [OpenCV](https://opencv.org/)
-- [FFmpeg](https://ffmpeg.org/)
-- This project is built as part of a research and development effort to analyze and detect video manipulations.
-
-## Contact
-- **Ayush Agarwal**  
-  AIML Enthusiast, VIT Bhopal University  
-  📧 [[email protected]](mailto:[email protected])  
-  🔗 [LinkedIn](https://www.linkedin.com/in/ayush20039939)  
-  🐙 [GitHub](https://github.com/your-github)
+
+- **DETR** (facebook/detr-resnet-50) model from Hugging Face for object detection.
+- OpenCV for frame extraction and optical flow calculations.
+- Streamlit for creating the interactive web interface.
 ```
 
-### Key Sections:
-1. **Project Overview**: Describes the purpose and features of the app.
-2. **Technologies Used**: Lists the libraries and tools used in the project.
-3. **Installation**: Step-by-step guide on how to clone, install dependencies, and run the app.
-4. **How to Use**: Instructions on how users can interact with the app.
-5. **Report Details**: Describes the information generated in the analysis report.
-6. **License & Acknowledgments**: Includes the MIT License and credits to the libraries and tools used.
+### Key Changes for Streamlit Space Deployment:
 
-This will help users understand the project and how to deploy and use it. Feel free to customize it further!
+1. **`sdk: streamlit`**: Specifies that this is a Streamlit-based application.
+2. **`app_file: app.py`**: Indicates the main file for the app (`app.py`).
+3. **Deployment Steps**: Instructions on how to deploy the app on Streamlit, including how to clone the repository and install dependencies.
+4. **Hugging Face Specific Configuration**: The configuration YAML ensures the app is correctly set up when deploying to **Hugging Face Spaces**.
+   
+Once this is set up, you'll be able to push your project to **Hugging Face Spaces**, where it will run as a **Streamlit** app.