Update app.py

app.py

@@ -1,73 +1,29 @@
 import gradio as gr
-import cv2
+import requests
 import numpy as np
-
-def process_frame(frame, bg_image):
-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)
-    lower_green = np.array([45, 100, 50])
-    upper_green = np.array([75, 255, 255])
-    mask = cv2.inRange(hsv, lower_green, upper_green)
-    mask_inv = cv2.bitwise_not(mask)
-    bg = cv2.imread(bg_image)
-    bg = cv2.resize(bg, (frame.shape[1], frame.shape[0]))
-    fg = cv2.bitwise_and(frame, frame, mask=mask_inv)
-    bg = cv2.bitwise_and(bg, bg, mask=mask)
-    result = cv2.add(bg, fg)
-    return result
-
-def remove_green_screen(input_video, bg_image):
-    cap = cv2.VideoCapture(input_video.name)
-    codec = cv2.VideoWriter_fourcc(*"mp4v")
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    out = cv2.VideoWriter("output.mp4", codec, fps, (frame_width, frame_height))
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-        result = process_frame(frame, bg_image)
-        out.write(result)
-    cap.release()
-    out.release()
-
-def predict(input_video, bg_image):
-    remove_green_screen(input_video, bg_image)
-    return "output.mp4"
-
-inputs = [
-    gr.inputs.Video(label="Input Video"),
-    gr.inputs.Image(label="Background Image")
-]
-
-outputs = gr.outputs.Video(label="Processed Video", type="auto")
-
-title = "Green Screen Remover"
-description = "Upload a video and an image to use as the background to remove the green screen."
-article = "<p style='text-align: center'><a href='https://github.com/gradio-app/examples/blob/master/green_screen_removal.py'>This code</a> was made into an interactive interface using Gradio. See the full tutorial at <a href='https://blog.gradio.app/green-screen-removal-with-opencv/'>this blog post</a> on the Gradio blog.</p>"
-examples = [
-    [
-        "https://www.youtube.com/watch?v=clD6_yXKo2I",
-        "https://i.imgur.com/lxIhsG6.jpg"
-    ],
-    [
-        "https://www.youtube.com/watch?v=6DfZ6UOZi0A",
-        "https://i.imgur.com/6UaTvfo.jpg"
-    ],
-]
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    analytics_enabled=False,
-    server_port=8000
-)
-
-if __name__ == '__main__':
-    iface.launch()
+from PIL import Image
+
+def enlarge_image(image, scale):
+    # convert image to bytes
+    img_byte_arr = image.tobytes()
+    # make request to DALL-E 2 API
+    response = requests.post("https://api.dall-e.com/v1/enlarge", 
+                             data=img_byte_arr,
+                             params={"size": scale})
+    # get the image bytes from the response
+    image_bytes = response.content
+    # convert bytes to numpy array
+    img_np = np.array(Image.open(BytesIO(image_bytes)))
+    return img_np
+
+# create the Gradio interface
+input_image = gr.inputs.Image(label="Input Image")
+scale = gr.inputs.Number(label="Scale", default=2)
+output_image = gr.outputs.Image(label="Enlarged Image")
+
+title = "DALL-E 2 Image Enlarger"
+description = "Enlarges an image using DALL-E 2. Enter a scale factor and upload an image."
+examples = [["examples/butterfly.jpg"]]
+
+gr.Interface(fn=enlarge_image, inputs=[input_image, scale], outputs=output_image,
+             title=title, description=description, examples=examples).launch()