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bba006e
1
Parent(s):
26787f1
app.py
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app.py
CHANGED
@@ -11,13 +11,15 @@ import gradio as gr
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# Load the openAI's CLIP model
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#model, preprocess = clip.load("ViT-B/32", jit=False)
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#display output photo
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def show_output_image(matched_images) :
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image=[]
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for photo_id in matched_images:
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#photo_image_url = f"https://unsplash.com/photos/{photo_id}/download?w=280"
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photo_image_url = f"https://unsplash.com/photos/{photo_id}?w=640"
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#photo_image_url = f"https://unsplash.com/photos/{photo_id}?ixid=2yJhcHBfaWQiOjEyMDd9&fm=jpg"
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img = Image.open(BytesIO(response.content))
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#return img
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image.append(img)
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@@ -42,6 +44,9 @@ def image_search(search_text, search_image, option):
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photo_ids = pd.read_csv("./photo_ids.csv")
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photo_ids = list(photo_ids['photo_id'])
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# taking features vectors
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photo_features = np.load("./features.npy")
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@@ -62,13 +67,13 @@ def image_search(search_text, search_image, option):
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# Find the matched Images
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matched_images = find_matches(text_features, photo_features, photo_ids, 4)
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# ---- debug purpose ------#
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print(matched_images[0])
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id = matched_images[0]
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photo_image_url = f"https://unsplash.com/photos/{id}/download?w=280"
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print(photo_image_url)
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#--------------------------#
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return show_output_image(matched_images)
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elif option == "Image-To-Image":
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# Input Image for Search
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with torch.no_grad():
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# Load the openAI's CLIP model
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#model, preprocess = clip.load("ViT-B/32", jit=False)
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#display output photo
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def show_output_image(matched_images, photos) :
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image=[]
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for photo_id in matched_images:
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#photo_image_url = f"https://unsplash.com/photos/{photo_id}/download?w=280"
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#photo_image_url = f"https://unsplash.com/photos/{photo_id}?w=640"
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#photo_image_url = f"https://unsplash.com/photos/{photo_id}?ixid=2yJhcHBfaWQiOjEyMDd9&fm=jpg"
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photo_found = photos[photos["photo_id"] == photo_id].iloc[0]
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response = requests.get(photo_found["photo_image_url"] + "?w=640")
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#response = requests.get(photo_image_url)
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img = Image.open(BytesIO(response.content))
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#return img
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image.append(img)
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photo_ids = pd.read_csv("./photo_ids.csv")
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photo_ids = list(photo_ids['photo_id'])
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# Photo dataset
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photos = pd.read_csv("./photos.tsv000", sep="\t", header=0)
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# taking features vectors
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photo_features = np.load("./features.npy")
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# Find the matched Images
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matched_images = find_matches(text_features, photo_features, photo_ids, 4)
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# ---- debug purpose ------#
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#print(matched_images[0])
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#id = matched_images[0]
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#photo_image_url = f"https://unsplash.com/photos/{id}/download?w=280"
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#print(photo_image_url)
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#--------------------------#
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return show_output_image(matched_images, photos)
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elif option == "Image-To-Image":
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# Input Image for Search
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with torch.no_grad():
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