Add the application file & required packages

.gitignore

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+### PyCharm+all ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
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+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
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+.idea/**/shelf
+
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+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
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+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
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+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
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+# CMake
+cmake-build-*/
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+.idea/**/mongoSettings.xml
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+
+# IntelliJ
+out/
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+# mpeltonen/sbt-idea plugin
+.idea_modules/
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+atlassian-ide-plugin.xml
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+com_crashlytics_export_strings.xml
+crashlytics.properties
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+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm+all Patch ###
+# Ignore everything but code style settings and run configurations
+# that are supposed to be shared within teams.
+
+.idea/*
+
+!.idea/codeStyles
+!.idea/runConfigurations
+
+### Python ###
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+# PyBuilder
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+# Jupyter Notebook
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+# IPython
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+# pyenv
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+# pipenv
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+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+# pdm
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+#pdm.lock
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+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
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+__pypackages__/
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+
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+.env
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+# mkdocs documentation
+/site
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+# mypy
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+.pyre/
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+# pytype static type analyzer
+.pytype/
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+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoProcessor
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+
+fashion_items = [
+    "top",
+    "trousers",
+    "jumper",
+    "t-shirt",
+    "jeans",
+    "sneakers",
+    "sweater",
+    "jacket",
+    "dress",
+    "blazer",
+    "boots",
+    "bag",
+    "sunglasses",
+]
+
+# Load model and processor
+model_name = "Marqo/marqo-fashionSigLIP"
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+# Preprocess and normalize text data
+with torch.no_grad():
+    # Ensure truncation and padding are activated
+    processed_texts = processor(
+        text=fashion_items,
+        return_tensors="pt",
+        truncation=True,  # Ensure text is truncated to fit model input size
+        padding=True,  # Pad shorter sequences so that all are the same length
+    )["input_ids"]
+
+    text_features = model.get_text_features(processed_texts)
+    text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+
+
+# Prediction function
+def predict_from_url(url):
+    # Check if the URL is empty
+    if not url:
+        return {"Error": "Please input a URL"}
+
+    try:
+        image = Image.open(BytesIO(requests.get(url).content))
+    except Exception as e:
+        return {"Error": f"Failed to load image: {str(e)}"}
+
+    processed_image = processor(images=image, return_tensors="pt")[
+        "pixel_values"
+    ]
+
+    with torch.no_grad():
+        image_features = model.get_image_features(processed_image)
+        image_features = image_features / image_features.norm(
+            dim=-1, keepdim=True
+        )
+        text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
+
+    return {
+        fashion_items[i]: float(text_probs[0, i])
+        for i in range(len(fashion_items))
+    }
+
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_from_url,
+    inputs=gr.Textbox(label="Enter Image URL"),
+    outputs=gr.Label(label="Classification Results"),
+    title="Fashion Item Classifier",
+    flagging_mode="never",
+)
+
+# Launch the interface
+demo.launch()

requirements.txt

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+transformers
+torch
+requests
+Pillow
+open_clip_torch
+ftfy
+
+# This is only needed for local deployment
+gradio