Create app.py

app.py

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+import streamlit as st
+import os
+import cv2
+import numpy as np
+from PIL import Image
+from rembg import remove
+from realesrgan import RealESRGAN
+from moviepy.editor import ImageSequenceClip
+
+# Upload section
+st.title("🖼️ AI Image & Video Effects Studio")
+st.markdown("Upload multiple images, apply cool effects, and export a video!")
+
+uploaded_files = st.file_uploader("Upload images", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
+
+# Select effect
+effect = st.selectbox("Choose an effect to apply", ["None", "Cartoon", "Edge Detection", "Remove Background", "Face Enhancement"])
+
+output_dir = "outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+processed_images = []
+
+def cartoonize(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = cv2.medianBlur(gray, 5)
+    edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
+                                  cv2.THRESH_BINARY, 9, 9)
+    color = cv2.bilateralFilter(image, 9, 300, 300)
+    cartoon = cv2.bitwise_and(color, color, mask=edges)
+    return cartoon
+
+def edge_detect(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 100, 200)
+    return cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
+
+def enhance_face(image_pil):
+    from realesrgan import RealESRGAN
+    model = RealESRGAN("cpu", scale=2)
+    model.load_weights("RealESRGAN_x2.pth")
+    return model.predict(image_pil)
+
+def save_video(images, filename="output_video.mp4"):
+    clip = ImageSequenceClip(images, fps=1)
+    clip.write_videofile(filename, codec="libx264")
+    return filename
+
+if uploaded_files:
+    for file in uploaded_files:
+        img = Image.open(file).convert("RGB")
+        np_img = np.array(img)
+        np_img_bgr = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+
+        if effect == "Cartoon":
+            result = cartoonize(np_img_bgr)
+        elif effect == "Edge Detection":
+            result = edge_detect(np_img_bgr)
+        elif effect == "Remove Background":
+            result = remove(np_img)
+        elif effect == "Face Enhancement":
+            result = enhance_face(img)
+        else:
+            result = np_img_bgr
+
+        result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if isinstance(result, np.ndarray) else result
+        final_img = Image.fromarray(result_rgb)
+
+        st.image(final_img, caption="Processed Image", use_column_width=True)
+
+        img_path = os.path.join(output_dir, f"{file.name}")
+        final_img.save(img_path)
+        processed_images.append(img_path)
+
+    # Convert to video
+    if st.button("🎞️ Convert to Video Slideshow"):
+        video_path = save_video(processed_images)
+        st.video(video_path)
+
+    # Download
+    for img_path in processed_images:
+        with open(img_path, "rb") as f:
+            st.download_button("Download Image", f, file_name=os.path.basename(img_path))
+