Upload app.py

app.py

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+import os
+import time
+import gradio as gr
+from gradio.themes import Size, GoogleFont
+import sys
+import pandas as pd
+import webbrowser
+from marqo import Client
+from PIL import Image
+import urllib.request
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("patrickjohncyh/fashion-clip")
+processor = CLIPProcessor.from_pretrained("patrickjohncyh/fashion-clip")
+
+# sys.path.insert(1, 'C:/Users/Alexandre/Documents/University/5_Ano/Estagio/repos_1')
+
+# Create custom Color objects for our primary, secondary, and neutral colors
+primary_color = gr.themes.colors.slate
+secondary_color = gr.themes.colors.rose
+neutral_color = gr.themes.colors.stone  # Assuming black for text
+# Set the sizes
+spacing_size = gr.themes.sizes.spacing_md
+radius_size = gr.themes.sizes.radius_md
+text_size = gr.themes.sizes.text_md
+# Set the fonts
+font = GoogleFont("Source Sans Pro")
+font_mono = GoogleFont("IBM Plex Mono")
+# Create the theme
+theme = gr.themes.Base(
+    primary_hue=primary_color,
+    secondary_hue=secondary_color,
+    neutral_hue=neutral_color,
+    spacing_size=spacing_size,
+    radius_size=radius_size,
+    text_size=text_size,
+    font=font,
+    font_mono=font_mono
+)
+
+def load_image(image_input):
+    image_input.save("../../../Documents/images/img_path.jpg")
+    os.system('docker cp "../../../Documents/images/img_path.jpg" marqo:"/images/images/"')
+
+
+def search_images(query, best_seller_score_weight):
+    client = Client()
+    result = client.index("multimodal").search(query, score_modifiers = {
+        "add_to_score": [{"field_name": "best_seller_score","weight": best_seller_score_weight/1000}],
+    }, searchable_attributes=['primary_image'], device="cpu", limit=5)
+    imgs = [r for r in result["hits"]]
+
+    return imgs
+
+def get_labels_probs(labels, image):
+    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
+
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+    probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+
+    return probs.tolist()[0]
+
+def get_bar_plot(labels, probs):
+    fig, ax = plt.subplots()
+    bar_container = ax.bar(labels, probs)
+    ax.set(ylabel='frequency', title='Labels probabilities\n', ylim=(0, 1))
+    ax.bar_label(bar_container, fmt='{:,.4f}')
+
+    return fig
+
+css = """
+.gradio-container {background-color: beige} 
+button.gallery-item {background-color: grey}
+.label {background-color: grey; width: 80px}
+h1 {background-color: grey; width: 180px}
+"""
+
+# css = """
+# .gradio-container {background-color: beige} 
+# .gallery-item {
+# """
+
+with gr.Blocks(theme=theme, title="New Look", css=css) as demo:
+    gr.Markdown(
+    """
+    <div style="vertical-align: middle">
+    <div style="float: left">
+    <img src="https://1000logos.net/wp-content/uploads/2021/05/New-Look-logo.png" alt=""
+    width="250" height="250">
+    </div>
+    </div>
+    """)
+
+    # gr.Markdown(
+    # """
+    # # Hello World!
+    # Start typing below to see the output.
+    # """, primary_color=gr.themes.colors.stone, secondary_color=gr.themes.colors.stone, neutral_color=gr.themes.colors.stone)
+
+    with gr.Tab(label="Search for images"):
+        # with gr.TabItem(label="Search for images"):
+        
+        with gr.Row().style(equal_height=False):
+            text_input = gr.Text(label="Search with text:")
+            text_relevance = gr.Slider(label="Text search relevance", minimum = -5, maximum = 5, value = 1, step = 1)
+            image_input = gr.Image(type="pil", label="Search with an image")
+            image_relevance = gr.Slider(label="Image search relevance", minimum = -5, maximum = 5, value = 1, step = 1)
+        with gr.Row():
+            gr.Examples(["Green", "Red", "Blue", "Sleeveless", "V-Neck", "Long dress, sleeveless, red"], text_input)
+            gr.Markdown()
+            gr.Examples(
+            ["../../../Documents/images/2272.jpg",
+            "../../../Documents/images/2697.jpg"],
+            image_input)
+            gr.Markdown()
+        # with gr.Row().style(equal_height=False):
+        #     gr.Markdown()
+        #     image_input = gr.Image(type="pil", label="Search with an image")
+        #     image_relevance = gr.Slider(label="Image search relevance", minimum = -5, maximum = 5, value = 1, step = 1)
+        #     gr.Markdown(scale=10)
+        # with gr.Row():
+        #     gr.Markdown()
+        #     gr.Examples(
+        #     ["../../../Documents/images/2272.jpg",
+        #     "../../../Documents/images/2697.jpg"],
+        #     image_input)
+        #     gr.Markdown()
+        #     gr.Markdown()
+        
+        with gr.Row():
+            gr.Markdown()
+            best_seller_score_weight = gr.Slider(label = "Best seller relevance", minimum=-1, maximum=1, value=0, step=0.01)
+            gr.Markdown()
+
+        
+        
+        # image_input = gr.Image(type="pil", label="Search with an image")
+        # image_relevance = gr.Slider(label="Image search relevance", minimum = -5, maximum = 5, value = 1, step = 1)
+        with gr.Row():
+            gr.Markdown()
+            search_button = gr.Button(value="Search")
+            gr.Markdown()
+
+        with gr.Row():
+            image_res_1 = gr.Image(type="pil")
+            image_res_2 = gr.Image(type="pil")
+            image_res_3 = gr.Image(type="pil")
+            image_res_4 = gr.Image(type="pil")
+            image_res_5 = gr.Image(type="pil")
+        response = gr.Text()
+
+    with gr.Tab(label="Search for images"):
+        labels_input = gr.Text(label="List of labels")
+        gr.Examples(
+        ["shirt, dress, shoe",
+         "short_sleeve, long_sleeve, three_quarter_sleeve, sleeveless, bell_sleeve"],
+        labels_input)
+        with gr.Row():
+            image_labels_input = gr.Image(type="pil", label="Image to compute")
+            bar_plot = gr.Plot()
+        with gr.Row():
+            gr.Examples(
+            ["../../../Documents/images/2272.jpg",
+            "../../../Documents/images/2697.jpg"],
+            image_labels_input)
+            gr.Markdown()
+        compute_button = gr.Button(value="Compute")
+
+        response_labels = gr.Text()
+    
+    with gr.Tab(label="Choose dataset"):
+        gr.Markdown("# Choose Dataset")
+        with gr.Row():
+            gr.Dropdown(["New Look Dresses", "New Look All"], label="Available datasets")
+            gr.Markdown()
+            gr.Markdown()
+        with gr.Row():
+            gr.Button("Select")
+            gr.Markdown()
+            gr.Markdown()
+
+
+    def search(text_input, image_input, text_relevance, image_relevance, best_seller_score_weight):
+        if text_input == "" and image_input == None:
+            empty_response = [None] * 5
+            empty_response.append("")
+            return empty_response
+        
+        if text_input == "":
+            load_image(image_input)
+            query = "/images/images/img_path.jpg"
+        elif image_input == None:
+            query = text_input
+        else:
+            query = dict()
+            load_image(image_input)
+            query["/images/images/img_path.jpg"] = image_relevance
+            query[text_input] = text_relevance
+
+        list_image_results = []
+        response = search_images(query, best_seller_score_weight)
+        for i in range(len(response)):
+            urllib.request.urlretrieve(response[i]["primary_image"], "../../../Documents/images/img_res_path_" + str(i) + ".jpg")
+            list_image_results.append(Image.open(r"../../../Documents/images/img_res_path_" + str(i) + r".jpg"))
+
+        return list_image_results[0], list_image_results[1], list_image_results[2], list_image_results[3], list_image_results[4], response
+    
+    def get_labels(labels_input, image_labels_input):
+        labels_probs = get_labels_probs(labels_input.split(","), image_labels_input)
+        bar_plot = get_bar_plot(labels_input.split(","), labels_probs)
+        return bar_plot, labels_probs
+        
+
+    search_button.click(
+        search, [text_input, image_input, text_relevance, image_relevance, best_seller_score_weight], [image_res_1, image_res_2, image_res_3, image_res_4, image_res_5, response]
+    )
+
+    compute_button.click(
+        get_labels, [labels_input, image_labels_input], [bar_plot, response_labels]
+    )
+
+    # image_input.upload(
+    #     user, image_input, image_input
+    # ).then(
+    #     respond, response, [image_res_1, image_res_2, image_res_3, image_res_4, image_res_5, response]
+    # )
+
+
+    # response = isComplete_state.change(
+    #     lambda: gr.update(interactive=False), None, [user_input], queue=False
+    # ).then(
+    #     respond_itinerary, [chatbot, isComplete_state, dataCollected_state], [chatbot, map, result_df]
+    # ).then(
+    #     lambda: gr.update(visible=True), None, [map], queue=False
+    # ).then(
+    #     lambda: gr.update(visible=True), None, [result_df], queue=False
+    # ).then(
+    #     lambda: gr.update(visible=False), None, [text_map_before_itinerary], queue=False
+    # )
+
+    # response.then(
+    #     lambda: gr.update(interactive=True), None, [user_input], queue=False
+    # )
+
+    # if map != None:
+    #     map.update(visible=True)
+    #     result_df.update(visible=True)
+
+demo.queue()
+demo.launch()