Test to clear cache

app.py

@@ -1,238 +1,243 @@
-import numpy as np
-from PIL import ImageDraw, Image, ImageFont
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-import torch
-import streamlit as st
-
-FONTS = [
-    "Font: Serif - EBGaramond",
-    "Font: Serif - Cinzel",
-    "Font: Sans - Roboto",
-    "Font: Sans - Lato",
-    "Font: Display - Lobster",
-    "Font: Display - LilitaOne",
-    "Font: Handwriting - GreatVibes",
-    "Font: Handwriting - Pacifico",
-    "Font: Mono - Inconsolata",
-    "Font: Mono - Cutive",
-]
-
-
-def hex_to_rgb(hex):
-    rgb = []
-    for i in (0, 2, 4):
-        decimal = int(hex[i : i + 2], 16)
-        rgb.append(decimal)
-    return tuple(rgb)
-
-
-@st.cache(allow_output_mutation=True)
-def load():
-    feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
-    model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
-    return model, feature_extractor
-
-
-model, feature_extractor = load()
-
-
-def compute_depth(image):
-    inputs = feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_depth = outputs.predicted_depth
-    prediction = torch.nn.functional.interpolate(
-        predicted_depth.unsqueeze(1),
-        size=image.size[::-1],
-        mode="bicubic",
-        align_corners=False,
-    )
-    return prediction.cpu().numpy()[0, 0, :, :]
-
-
-def get_mask1(
-    shape, x, y, caption, font=None, font_size=0.08, color=(0, 0, 0), alpha=0.8
-):
-    img_text = Image.new("RGBA", (shape[1], shape[0]), (0, 0, 0, 0))
-    draw = ImageDraw.Draw(img_text)
-    font = ImageFont.truetype(font, int(font_size * shape[1]))
-    draw.text(
-        (x * shape[1], (1 - y) * shape[0]),
-        caption,
-        fill=(*color, int(max(min(1, alpha), 0) * 255)),
-        font=font,
-    )
-    text = np.array(img_text)
-    mask1 = np.dot(np.expand_dims(text[:, :, -1] / 255, -1), np.ones((1, 3)))
-    return text[:, :, :-1], mask1
-
-
-def get_mask2(depth_map, depth):
-    return np.expand_dims(
-        (depth_map[:, :] < depth * np.min(depth_map) + (1 - depth) * np.max(depth_map)),
-        -1,
-    )
-
-
-def add_caption(
-    img,
-    caption,
-    depth_map=None,
-    x=0.5,
-    y=0.5,
-    depth=0.5,
-    font_size=50,
-    color=(255, 255, 255),
-    font="",
-    alpha=1,
-):
-    text, mask1 = get_mask1(
-        img.shape,
-        x,
-        y,
-        caption,
-        font=font,
-        font_size=font_size,
-        color=color,
-        alpha=alpha,
-    )
-    mask2 = get_mask2(depth_map, depth)
-    mask = mask1 * np.dot(mask2, np.ones((1, 3)))
-
-    return ((1 - mask) * img + mask * text).astype(np.uint8)
-
-
-@st.cache(max_entries=30, show_spinner=False)
-def load_img(uploaded_file):
-    if uploaded_file is None:
-        img = Image.open("pulp.jpg")
-        default = True
-    else:
-        img = Image.open(uploaded_file)
-        if img.size[0] > 800 or img.size[1] > 800:
-            if img.size[0] < img.size[1]:
-                new_size = (int(800 * img.size[0] / img.size[1]), 800)
-            else:
-                new_size = (800, int(800 * img.size[1] / img.size[0]))
-            img = img.resize(new_size)
-        default = False
-    return np.array(img), compute_depth(img), default
-
-
-def main():
-    st.markdown(
-        """
-    <style>
-        label{
-            height: 0px !important;
-            min-height: 0px !important;
-            margin-bottom: 0px !important;
-        }
-    </style>
-        """,
-        unsafe_allow_html=True,
-    )
-
-    st.sidebar.markdown(
-        """
-    # Depth-aware text addition
-
-    Add text ***inside*** an image!
-
-    Upload an image, enter some text and adjust the ***depth*** where you want the text to be displayed. You can also define its location and appearance (font, color, transparency and size).
-
-    Built with [PyTorch](https://pytorch.org/), Intel's [MiDaS model](https://pytorch.org/hub/intelisl_midas_v2/), [Streamlit](https://streamlit.io/), [pillow](https://python-pillow.org/) and inspired by the official [video](https://youtu.be/eTa1jHk1Lxc) of *Jenny of Oldstones* by Florence + the Machine 
-    """
-    )
-
-    uploaded_file = st.file_uploader("", type=["jpg", "jpeg"])
-    with st.spinner("Analyzing the image - Please wait a few seconds"):
-        img, depth_map, default = load_img(uploaded_file)
-
-    if default:
-        x0, y0, alpha0, font_size0, depth0, font0 = 0.02, 0.68, 0.99, 0.07, 0.12, 4
-        text0 = "Pulp Fiction"
-    else:
-        x0, y0, alpha0, font_size0, depth0, font0 = 0.1, 0.9, 0.8, 0.08, 0.5, 0
-        text0 = "Enter your text here"
-
-    colA, colB, colC = st.columns((13, 1, 1))
-
-    with colA:
-        text = st.text_input("", text0)
-
-    with colB:
-        st.markdown("Color:")
-
-    with colC:
-        color = st.color_picker("", value="#FFFFFF")
-
-    col1, _, col2 = st.columns((4, 1, 4))
-
-    with col1:
-        depth = st.select_slider(
-            "",
-            options=[i / 100 for i in range(101)],
-            value=depth0,
-            format_func=lambda x: "Foreground"
-            if x == 0.0
-            else "Background"
-            if x == 1.0
-            else "",
-        )
-        x = st.select_slider(
-            "",
-            options=[i / 100 for i in range(101)],
-            value=x0,
-            format_func=lambda x: "Left" if x == 0.0 else "Right" if x == 1.0 else "",
-        )
-        y = st.select_slider(
-            "",
-            options=[i / 100 for i in range(101)],
-            value=y0,
-            format_func=lambda x: "Bottom" if x == 0.0 else "Top" if x == 1.0 else "",
-        )
-
-    with col2:
-        font_size = st.select_slider(
-            "",
-            options=[0.04 + i / 100 for i in range(0, 17)],
-            value=font_size0,
-            format_func=lambda x: "Small font"
-            if x == 0.04
-            else "Large font"
-            if x == 0.2
-            else "",
-        )
-        alpha = st.select_slider(
-            "",
-            options=[i / 100 for i in range(101)],
-            value=alpha0,
-            format_func=lambda x: "Transparent"
-            if x == 0.0
-            else "Opaque"
-            if x == 1.0
-            else "",
-        )
-        font = st.selectbox("", FONTS, index=font0)
-
-    font = f"fonts/{font[6:]}.ttf"
-
-    captioned = add_caption(
-        img,
-        text,
-        x=x,
-        y=y,
-        depth=depth,
-        depth_map=depth_map,
-        font=font,
-        font_size=font_size,
-        alpha=alpha,
-        color=hex_to_rgb(color[1:]),
-    )
-
-    st.image(captioned)
-
-
-if __name__ == "__main__":
-    main()
+from streamlit import legacy_caching
+
+legacy_caching.clear_cache()
+
+
+# import numpy as np
+# from PIL import ImageDraw, Image, ImageFont
+# from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+# import torch
+# import streamlit as st
+
+# FONTS = [
+#     "Font: Serif - EBGaramond",
+#     "Font: Serif - Cinzel",
+#     "Font: Sans - Roboto",
+#     "Font: Sans - Lato",
+#     "Font: Display - Lobster",
+#     "Font: Display - LilitaOne",
+#     "Font: Handwriting - GreatVibes",
+#     "Font: Handwriting - Pacifico",
+#     "Font: Mono - Inconsolata",
+#     "Font: Mono - Cutive",
+# ]
+
+
+# def hex_to_rgb(hex):
+#     rgb = []
+#     for i in (0, 2, 4):
+#         decimal = int(hex[i : i + 2], 16)
+#         rgb.append(decimal)
+#     return tuple(rgb)
+
+
+# @st.cache(allow_output_mutation=True)
+# def load():
+#     feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
+#     model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+#     return model, feature_extractor
+
+
+# model, feature_extractor = load()
+
+
+# def compute_depth(image):
+#     inputs = feature_extractor(images=image, return_tensors="pt")
+#     with torch.no_grad():
+#         outputs = model(**inputs)
+#         predicted_depth = outputs.predicted_depth
+#     prediction = torch.nn.functional.interpolate(
+#         predicted_depth.unsqueeze(1),
+#         size=image.size[::-1],
+#         mode="bicubic",
+#         align_corners=False,
+#     )
+#     return prediction.cpu().numpy()[0, 0, :, :]
+
+
+# def get_mask1(
+#     shape, x, y, caption, font=None, font_size=0.08, color=(0, 0, 0), alpha=0.8
+# ):
+#     img_text = Image.new("RGBA", (shape[1], shape[0]), (0, 0, 0, 0))
+#     draw = ImageDraw.Draw(img_text)
+#     font = ImageFont.truetype(font, int(font_size * shape[1]))
+#     draw.text(
+#         (x * shape[1], (1 - y) * shape[0]),
+#         caption,
+#         fill=(*color, int(max(min(1, alpha), 0) * 255)),
+#         font=font,
+#     )
+#     text = np.array(img_text)
+#     mask1 = np.dot(np.expand_dims(text[:, :, -1] / 255, -1), np.ones((1, 3)))
+#     return text[:, :, :-1], mask1
+
+
+# def get_mask2(depth_map, depth):
+#     return np.expand_dims(
+#         (depth_map[:, :] < depth * np.min(depth_map) + (1 - depth) * np.max(depth_map)),
+#         -1,
+#     )
+
+
+# def add_caption(
+#     img,
+#     caption,
+#     depth_map=None,
+#     x=0.5,
+#     y=0.5,
+#     depth=0.5,
+#     font_size=50,
+#     color=(255, 255, 255),
+#     font="",
+#     alpha=1,
+# ):
+#     text, mask1 = get_mask1(
+#         img.shape,
+#         x,
+#         y,
+#         caption,
+#         font=font,
+#         font_size=font_size,
+#         color=color,
+#         alpha=alpha,
+#     )
+#     mask2 = get_mask2(depth_map, depth)
+#     mask = mask1 * np.dot(mask2, np.ones((1, 3)))
+
+#     return ((1 - mask) * img + mask * text).astype(np.uint8)
+
+
+# @st.cache(max_entries=30, show_spinner=False)
+# def load_img(uploaded_file):
+#     if uploaded_file is None:
+#         img = Image.open("pulp.jpg")
+#         default = True
+#     else:
+#         img = Image.open(uploaded_file)
+#         if img.size[0] > 800 or img.size[1] > 800:
+#             if img.size[0] < img.size[1]:
+#                 new_size = (int(800 * img.size[0] / img.size[1]), 800)
+#             else:
+#                 new_size = (800, int(800 * img.size[1] / img.size[0]))
+#             img = img.resize(new_size)
+#         default = False
+#     return np.array(img), compute_depth(img), default
+
+
+# def main():
+#     st.markdown(
+#         """
+#     <style>
+#         label{
+#             height: 0px !important;
+#             min-height: 0px !important;
+#             margin-bottom: 0px !important;
+#         }
+#     </style>
+#         """,
+#         unsafe_allow_html=True,
+#     )
+
+#     st.sidebar.markdown(
+#         """
+#     # Depth-aware text addition
+
+#     Add text ***inside*** an image!
+
+#     Upload an image, enter some text and adjust the ***depth*** where you want the text to be displayed. You can also define its location and appearance (font, color, transparency and size).
+
+#     Built with [PyTorch](https://pytorch.org/), Intel's [MiDaS model](https://pytorch.org/hub/intelisl_midas_v2/), [Streamlit](https://streamlit.io/), [pillow](https://python-pillow.org/) and inspired by the official [video](https://youtu.be/eTa1jHk1Lxc) of *Jenny of Oldstones* by Florence + the Machine 
+#     """
+#     )
+
+#     uploaded_file = st.file_uploader("", type=["jpg", "jpeg"])
+#     with st.spinner("Analyzing the image - Please wait a few seconds"):
+#         img, depth_map, default = load_img(uploaded_file)
+
+#     if default:
+#         x0, y0, alpha0, font_size0, depth0, font0 = 0.02, 0.68, 0.99, 0.07, 0.12, 4
+#         text0 = "Pulp Fiction"
+#     else:
+#         x0, y0, alpha0, font_size0, depth0, font0 = 0.1, 0.9, 0.8, 0.08, 0.5, 0
+#         text0 = "Enter your text here"
+
+#     colA, colB, colC = st.columns((13, 1, 1))
+
+#     with colA:
+#         text = st.text_input("", text0)
+
+#     with colB:
+#         st.markdown("Color:")
+
+#     with colC:
+#         color = st.color_picker("", value="#FFFFFF")
+
+#     col1, _, col2 = st.columns((4, 1, 4))
+
+#     with col1:
+#         depth = st.select_slider(
+#             "",
+#             options=[i / 100 for i in range(101)],
+#             value=depth0,
+#             format_func=lambda x: "Foreground"
+#             if x == 0.0
+#             else "Background"
+#             if x == 1.0
+#             else "",
+#         )
+#         x = st.select_slider(
+#             "",
+#             options=[i / 100 for i in range(101)],
+#             value=x0,
+#             format_func=lambda x: "Left" if x == 0.0 else "Right" if x == 1.0 else "",
+#         )
+#         y = st.select_slider(
+#             "",
+#             options=[i / 100 for i in range(101)],
+#             value=y0,
+#             format_func=lambda x: "Bottom" if x == 0.0 else "Top" if x == 1.0 else "",
+#         )
+
+#     with col2:
+#         font_size = st.select_slider(
+#             "",
+#             options=[0.04 + i / 100 for i in range(0, 17)],
+#             value=font_size0,
+#             format_func=lambda x: "Small font"
+#             if x == 0.04
+#             else "Large font"
+#             if x == 0.2
+#             else "",
+#         )
+#         alpha = st.select_slider(
+#             "",
+#             options=[i / 100 for i in range(101)],
+#             value=alpha0,
+#             format_func=lambda x: "Transparent"
+#             if x == 0.0
+#             else "Opaque"
+#             if x == 1.0
+#             else "",
+#         )
+#         font = st.selectbox("", FONTS, index=font0)
+
+#     font = f"fonts/{font[6:]}.ttf"
+
+#     captioned = add_caption(
+#         img,
+#         text,
+#         x=x,
+#         y=y,
+#         depth=depth,
+#         depth_map=depth_map,
+#         font=font,
+#         font_size=font_size,
+#         alpha=alpha,
+#         color=hex_to_rgb(color[1:]),
+#     )
+
+#     st.image(captioned)
+
+
+# if __name__ == "__main__":
+#     main()