Update app.py

"딥페이크 느낌나게"

app.py

@@ -1,57 +1,47 @@
 import streamlit as st
-import cv2
-import numpy as np
+import requests
 from PIL import Image
+import numpy as np
+import io
 
-def change_hair_to_blonde(image):
-    # Convert to OpenCV format
-    image = np.array(image)
-    # Convert the image to HSV color space
-    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
-
-    # Define the range for hair color (dark colors)
-    lower_hair = np.array([0, 0, 0])
-    upper_hair = np.array([180, 255, 30])
-
-    # Create a mask for hair
-    mask = cv2.inRange(hsv, lower_hair, upper_hair)
-
-    # Change hair color to blonde (light yellow)
-    hsv[mask > 0] = (30, 255, 200)
+# Function to apply deepfake-like transformation using an API
+def apply_deepfake(image):
+    # Convert PIL image to bytes
+    image_bytes = io.BytesIO()
+    image.save(image_bytes, format='JPEG')
+    image_bytes = image_bytes.getvalue()
 
-    # Convert back to RGB color space
-    image_blonde = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
-    return image_blonde
+    # Call the Hugging Face API
+    api_url = "https://api-inference.huggingface.co/models/spaces/dalle-mini/dalle-mini"
+    headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_API_TOKEN"}
+    response = requests.post(api_url, headers=headers, files={"file": image_bytes})
 
-def add_noise(image):
-    # Convert to OpenCV format
-    image_np = np.array(image)
-    # Generate random noise
-    noise = np.random.normal(0, 25, image_np.shape).astype(np.uint8)
-    # Add noise to the image
-    noisy_image = cv2.add(image_np, noise)
-    return noisy_image
+    # Convert response to image
+    response_image = Image.open(io.BytesIO(response.content))
+    return response_image
 
-st.title("딥페이크 사전 방지 필터 씌우기")
+st.title("Image Processing MVP")
 
-uploaded_file = st.file_uploader("필터를 씌울 이미지를 업로드하세요...", type=["jpg", "png", "jpeg"])
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
 
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
-    st.image(image, caption='노이즈 씌운 이미지', use_column_width=True)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
     st.write("")
     st.write("Processing...")
 
-    # Save the original image as a numpy array
-    image_np = np.array(image)
-
-    action = st.radio("Choose an action:", ('A', 'B'))
+    action = st.radio("Choose an action:", ('A', 'B', 'Deepfake'))
 
-    if action == '원본 사진이 딥페이크에 입력된 경우':
-        # Change hair color to blonde
-        processed_image = change_hair_to_blonde(image)
-        st.image(processed_image, caption='원본 사진이 딥페이크에 입력된 경우', use_column_width=True)
-    elif action == '방어된 사진이 딥페이크에 입력된 경우':
+    if action == 'A':
+        # Just display the original image
+        st.image(image, caption='Original Image.', use_column_width=True)
+    elif action == 'B':
         # Add noise to the original image
-        processed_image = add_noise(image_np)
-        st.image(processed_image, caption='방어된 사진이 딥페이크에 입력된 경우', use_column_width=True)
+        image_np = np.array(image)
+        noise = np.random.normal(0, 25, image_np.shape).astype(np.uint8)
+        noisy_image = cv2.add(image_np, noise)
+        st.image(noisy_image, caption='Image with Noise.', use_column_width=True)
+    elif action == 'Deepfake':
+        # Apply deepfake transformation
+        deepfake_image = apply_deepfake(image)
+        st.image(deepfake_image, caption='Deepfake Image.', use_column_width=True)