Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
-import numpy as np # linear algebra
-import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv
+import numpy as np  # linear algebra
+import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv
 
 import tensorflow as tf
 import cv2
@@ -11,38 +11,72 @@ warnings.filterwarnings('ignore')
 
 # Set Page Config
 st.set_page_config(
-    page_title = 'DataFlow App',
-    page_icon = '📊',
-    layout = 'wide',
-    initial_sidebar_state = 'auto',
+    page_title='ReDeepFake Demo App',
+    page_icon='📊',
+    layout='wide',
+    initial_sidebar_state='auto',
 )
 
+
 @st.cache_resource()
 def load_model():
     model = tf.keras.models.load_model('models/model.h5')
     return model
 
+
 def preprocess_image(image):
-    img = cv2.resize(image, (224, 224)) 
+    img = cv2.resize(image, (224, 224))
     img = tf.keras.applications.efficientnet.preprocess_input(img)
     return img
 
 # Home Page Content
 def main():
     model = load_model()
-    threshold = 0.33
 
     with st.sidebar:
         # Set Sidebar Content
         st.sidebar.image('media/logo.png', use_column_width=True)
         with st.container():
-            img_uploaded = st.file_uploader("Choose an image...", type=["jpg", "png"])
-        st.info('ReDeepFake is an advanced Deepfake detection model for 2D flat images.')
+            img_uploaded = st.file_uploader(
+                "Choose an image...", type=["jpg", "png"])
+            threshold = st.select_slider(
+                'Threshold', options=[i/100 for i in range(0, 101, 5)], value=0.5)
+        st.info(
+            'ReDeepFake is an advanced Deepfake detection model for 2D flat images.')
         st.caption('Made with ❤️ by [DataFlow](https://dataflow.kz) team.')
-    
+
+    st.title('🧑 ReDeepFake v1.4')
+    st.markdown('''
+        This is a demo app for ReDeepFake model - Advanced Deepfake detection model for 2D flat images.
+    ''')
+    colA, colB = st.columns(2)
+    colA.markdown('''
+        **How to use this app?**
+        1. Upload an image.
+        2. Adjust the threshold.
+        3. Image will be processed and real/fake faces will be detected automatically.            
+    ''')
+    colB.markdown('''
+        **Resources:**
+        * **Kaggle Notebook:** [ReDeepFake](https://www.kaggle.com/code/armanzhalgasbayev/deepfake-detection-efficientnetb4-tf-cnn)
+        * **GitHub Repository:** [ReDeepFake](https://github.com/silvermete0r/redeepfake-demo-app)
+        * **Hugging Face Demo App:** [ReDeepFake](https://huggingface.co/spaces/dataflow/redeepfake-demo)
+        * **Download Model:** [ReDeepFake](https://huggingface.co/dataflow/redeepfake)
+    ''')
+    st.caption('**Note:** Before using the model read about limitations.')
+    with st.expander('Limitations', expanded=False):
+        st.markdown('''
+            1. The model's performance may be influenced by variations in lighting conditions, image quality, and diverse facial expressions.
+
+            2. It may not be fully robust against emerging deepfake generation techniques: Modern image generation methods use advanced descriptors to assess the quality of the image's realism, so photos of such faces are difficult to distinguish from real people. 
+
+            3. Deepfakes made by using 3D image processing technologies and manually modified images by the authors cannot be recognized correctly by the model.
+        ''')
+        
     if img_uploaded is not None:
         with st.spinner('Processing the image, getting faces...'):
-            image = cv2.imdecode(np.frombuffer(img_uploaded.read(), dtype=np.uint8), 1)
+            image = cv2.imdecode(np.frombuffer(
+                img_uploaded.read(), dtype=np.uint8), 1)
             face_locations = face_recognition.face_locations(image)
         if len(face_locations) == 0:
             st.warning('Faces not found!')
@@ -55,16 +89,23 @@ def main():
 
                 prediction = model.predict(processed_face)
 
-                predicted_class = "FAKE" if prediction[0, 0] > threshold else "REAL"
+                predicted_class = "FAKE" if prediction[0,
+                                                       0] > threshold else "REAL"
 
-                st.image(cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB), caption=f"Face {i+1}: {predicted_class} | Score: {prediction[0, 0]:.2f}", width=350)
+                st.image(cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB),
+                         caption=f"Face {i+1}: {predicted_class} | Score: {prediction[0, 0]:.2f}", width=350)
+
+                download_img = cv2.imencode('.png', face_image)[1].tobytes()
+                st.download_button(label="Download Image", data=download_img,
+                                   file_name=f"face_{i+1}_{predicted_class}.png", mime="image/png")
 
                 if predicted_class == "FAKE":
                     st.warning('The image is most likely fake!')
                 else:
                     st.success('The image is most likely real!')
-                
 
-if __name__ == "__main__":
-    main()
+                st.divider()
 
+
+if __name__ == "__main__":
+    main()