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README.md

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----
-title: Deep Fake Detection Model
-emoji: 📈
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.42.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Machine Learning Model used to detect the deep fake for user
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# DeepFake Detection Model
+
+This repository hosts a **DeepFake Detection Model** deployed using **Streamlit** on Hugging Face Spaces. The model is trained to classify images as **REAL or FAKE** using Convolutional Neural Networks (CNNs).
+
+## 🚀 Live Demo
+Check out the deployed model: [DeepFake Detector on Hugging Face](https://huggingface.co/spaces/your-username/deepfake-detection)
+
+## 📌 Features
+- **Deep Learning-Based Detection**: Uses a pre-trained CNN model to detect DeepFake images.
+- **User-Friendly UI**: Built with Streamlit for a simple and interactive experience.
+- **Easy API Integration**: Allows users to upload images and get predictions.
+
+## 🛠 Installation & Setup
+To run the project locally, follow these steps:
+
+### 1️⃣ Clone the Repository
+git clone https://huggingface.co/spaces/your-username/deepfake-detection
+cd deepfake-detection
+
+### 2️⃣ Install Dependencies
+Ensure you have **Python 3.8+** installed. Install dependencies using:
+pip install -r requirements.txt
+
+### 3️⃣ Run the Application
+streamlit run app.py
+
+## 📁 Project Structure
+```
+📂 deepfake-detection
+├── 📜 app.py             # Main Streamlit app
+├── 📜 requirements.txt   # Dependencies
+├── 📜 README.md          # Documentation
+├── 📜 deep-fake-detection-on-image-fyp.ipynb
+
+## 🧠 Model Details
+- **Model Used**: Xception-based CNN
+- **Input Shape**: (256, 256, 3)
+- **Output**: Binary classification (REAL or FAKE)
+
+## 🖼️ How to Use
+1. Upload an image of a face.
+2. The model will process the image and classify it as **REAL** or **FAKE**.
+3. The result will be displayed on the screen.
+
+## 🌐 Deploying on Hugging Face
+If you want to deploy your own version:
+1. Create a new Space on Hugging Face.
+2. Upload `app.py`, `requirements.txt`, and your model file.
+3. Set the Space **Runtime** to `Streamlit`.
+4. Click **Restart** to launch the app.
+
+## 🤝 Contributing
+Feel free to fork this repository and improve the model! Pull requests are welcome. 😊
+
+## 📜 License
+This project is licensed under the MIT License.
+
+
+Developed by **Muneeb Nadeem** | Contact: [muneebnadeem1870@gmail.com]

app.py

@@ -1,7 +1,7 @@
-import streamlit as st
-import numpy as np
-from tensorflow.keras.models import load_model
-from PIL import Image
+import streamlit as st # type: ignore
+import numpy as np # type: ignore
+from tensorflow.keras.models import load_model # type: ignore
+from PIL import Image # type: ignore
 
 # Model Load
 model = load_model("xception_deepfake_image.h5")