Delete app.py

app.py

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-import gradio as gr
-import open_clip
-import torch
-import requests
-import numpy as np
-from PIL import Image
-from io import BytesIO
-
-# Sidebar content
-sidebar_markdown = """
-Note, this demo can classify 200 items. If you didn't find what you're looking for, reach out to us on our [Community](https://join.slack.com/t/marqo-community/shared_invite/zt-2iab0260n-QJrZLUSOJYUifVxf964Gdw) and request an item to be added.
-
-## Documentation
-📚 [Blog Post](https://www.marqo.ai/blog/search-model-for-fashion)
-
-📝 [Use Case Blog Post](https://www.marqo.ai/blog/ecommerce-image-classification-with-marqo-fashionclip)
-
-## Code
-💻 [GitHub Repo](https://github.com/marqo-ai/marqo-FashionCLIP)
-
-🤝 [Google Colab](https://colab.research.google.com/drive/1nq978xFJjJcnyrJ2aE5l82GHAXOvTmfd?usp=sharing)
-
-🤗 [Hugging Face Collection](https://huggingface.co/collections/Marqo/marqo-fashionclip-and-marqo-fashionsiglip-66b43f2d09a06ad2368d4af6)
-"""
-
-# List of fashion items and their IDs
-categories = [
-    {"name": "Nettoyants visage", "id": 101},
-    {"name": "Exfoliants visage", "id": 102},
-    {"name": "Hydratants visage", "id": 103},
-    {"name": "Masques visage", "id": 104},
-    {"name": "Soins ciblés visage", "id": 105},
-    {"name": "Protection solaire visage", "id": 106},
-    {"name": "Nettoyants visage homme", "id": 107},
-    {"name": "Crèmes hydratantes homme", "id": 108},
-    {"name": "Soins après-rasage", "id": 109},
-    {"name": "Hydratants corps", "id": 110},
-    {"name": "Exfoliants corps", "id": 111},
-    {"name": "Soins fermeté & minceur", "id": 112},
-    {"name": "Auto-bronzants", "id": 113},
-    {"name": "Soins des mains", "id": 114},
-    {"name": "Soins des pieds", "id": 115},
-    {"name": "Hydratants corps homme", "id": 116},
-    {"name": "Déodorants corps homme", "id": 117},
-    {"name": "Shampoings", "id": 118},
-    {"name": "Après-shampoings", "id": 119},
-    {"name": "Masques capillaires", "id": 120},
-    {"name": "Huiles capillaires", "id": 121},
-    {"name": "Coiffants", "id": 122},
-    {"name": "Accessoires cheveux", "id": 123},
-    {"name": "Soins cheveux homme", "id": 124},
-    {"name": "Produits coiffants homme", "id": 125},
-    {"name": "Fond de teint", "id": 126},
-    {"name": "BB/CC crèmes", "id": 127},
-    {"name": "Poudres", "id": 128},
-    {"name": "Fards à paupières", "id": 129},
-    {"name": "Mascaras", "id": 130},
-    {"name": "Eyeliners", "id": 131},
-    {"name": "Rouges à lèvres", "id": 132},
-    {"name": "Gloss", "id": 133},
-    {"name": "Crayons à sourcils", "id": 134},
-    {"name": "Accessoires maquillage", "id": 135},
-    {"name": "Correcteurs teint homme", "id": 136},
-    {"name": "Poudres matifiantes homme", "id": 137},
-    {"name": "Parfums", "id": 138},
-    {"name": "Brumes corporelles", "id": 139},
-    {"name": "Huiles essentielles", "id": 140},
-    {"name": "Diffuseurs d'huiles", "id": 141},
-    {"name": "Bougies parfumées", "id": 142},
-    {"name": "Déodorants solides", "id": 143},
-    {"name": "Déodorants sprays", "id": 144},
-    {"name": "Savons solides", "id": 145},
-    {"name": "Savons liquides", "id": 146},
-    {"name": "Produits bain", "id": 147},
-    {"name": "Hygiène intime", "id": 148},
-    {"name": "Cups menstruelles", "id": 149},
-    {"name": "Produits zéro déchet", "id": 150},
-    {"name": "Brosses nettoyantes visage", "id": 151},
-    {"name": "Pinces à épiler", "id": 152},
-    {"name": "Trousse de voyage", "id": 153},
-    {"name": "Huiles de CBD", "id": 154},
-    {"name": "Cosmétiques au CBD", "id": 155},
-    {"name": "Infusions au CBD", "id": 156},
-    {"name": "Bonbons au CBD", "id": 157},
-    {"name": "Accessoires CBD", "id": 158},
-    {"name": "Robes femme", "id": 201},
-    {"name": "Tops femme", "id": 202},
-    {"name": "Chemisiers femme", "id": 203},
-    {"name": "T-shirts femme", "id": 204},
-    {"name": "Pulls femme", "id": 205},
-    {"name": "Jeans femme", "id": 206},
-    {"name": "Pantalons femme", "id": 207},
-    {"name": "Jupes femme", "id": 208},
-    {"name": "Shorts femme", "id": 209},
-    {"name": "Vestes femme", "id": 210},
-    {"name": "Manteaux femme", "id": 211},
-    {"name": "Maillots de bain femme", "id": 212},
-    {"name": "Lingerie femme", "id": 213},
-    {"name": "Chaussures femme", "id": 214},
-    {"name": "Sacs femme", "id": 215},
-    {"name": "Bijoux femme", "id": 216},
-    {"name": "Chemises homme", "id": 301},
-    {"name": "T-shirts homme", "id": 302},
-    {"name": "Polos homme", "id": 303},
-    {"name": "Pulls homme", "id": 304},
-    {"name": "Jeans homme", "id": 305},
-    {"name": "Pantalons homme", "id": 306},
-    {"name": "Shorts homme", "id": 307},
-    {"name": "Vestes homme", "id": 308},
-    {"name": "Manteaux homme", "id": 309},
-    {"name": "Costumes homme", "id": 310},
-    {"name": "Maillots de bain homme", "id": 311},
-    {"name": "Sous-vêtements homme", "id": 312},
-    {"name": "Chaussures homme", "id": 313},
-    {"name": "Accessoires homme", "id": 314},
-    {"name": "Montres homme", "id": 315},
-    {"name": "Vêtements bébé (0-2 ans)", "id": 401},
-    {"name": "T-shirts enfant", "id": 402},
-    {"name": "Pulls enfant", "id": 403},
-    {"name": "Pantalons enfant", "id": 404},
-    {"name": "Robes enfant", "id": 405},
-    {"name": "Jeans enfant", "id": 406},
-    {"name": "Vestes enfant", "id": 407},
-    {"name": "Pyjamas enfant", "id": 408},
-    {"name": "Chaussures enfant", "id": 409},
-    {"name": "Accessoires enfant", "id": 410},
-    {"name": "Vêtements de sport enfant", "id": 411},
-    {"name": "Maillots de bain enfant", "id": 412},
-    {"name": "Sous-vêtements enfant", "id": 413},
-    {"name": "Déguisements enfant", "id": 414},
-    {"name": "Cartables et sacs enfant", "id": 415},
-    # Chaussures Femme détaillées
-    {"name": "Sneakers femme", "id": 217},
-    {"name": "Boots femme", "id": 218},
-    {"name": "Escarpins femme", "id": 219},
-    {"name": "Sandales femme", "id": 220},
-    {"name": "Ballerines femme", "id": 221},
-    {"name": "Mocassins femme", "id": 222},
-    {"name": "Bottines femme", "id": 223},
-    {"name": "Espadrilles femme", "id": 224},
-    {"name": "Mules femme", "id": 225},
-    {"name": "Chaussures de sport femme", "id": 226},
-    {"name": "Bottes hautes femme", "id": 227},
-    {"name": "Chaussures compensées femme", "id": 228},
-    # Chaussures Homme détaillées
-    {"name": "Sneakers homme", "id": 316},
-    {"name": "Boots homme", "id": 317},
-    {"name": "Chaussures de ville homme", "id": 318},
-    {"name": "Mocassins homme", "id": 319},
-    {"name": "Sandales homme", "id": 320},
-    {"name": "Chaussures bateau homme", "id": 321},
-    {"name": "Bottines homme", "id": 322},
-    {"name": "Chaussures de sport homme", "id": 323},
-    {"name": "Espadrilles homme", "id": 324},
-    {"name": "Derbies homme", "id": 325},
-    {"name": "Richelieus homme", "id": 326},
-    {"name": "Chaussures de randonnée homme", "id": 327},
-    # Chaussures Enfant détaillées
-    {"name": "Sneakers enfant", "id": 416},
-    {"name": "Bottes enfant", "id": 417},
-    {"name": "Sandales enfant", "id": 418},
-    {"name": "Chaussures de sport enfant", "id": 419},
-    {"name": "Chaussures premiers pas", "id": 420},
-    {"name": "Chaussures à scratch enfant", "id": 421},
-    {"name": "Chaussures d'école enfant", "id": 422},
-    {"name": "Pantoufles enfant", "id": 423},
-    {"name": "Chaussures de cérémonie enfant", "id": 424},
-    {"name": "Bottes de pluie enfant", "id": 425}
-];
-
-
-# Extract category names
-items = [category["name"] for category in categories]
-
-# Initialize the model and tokenizer
-model_name = 'hf-hub:Marqo/marqo-fashionSigLIP'
-model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms(model_name)
-tokenizer = open_clip.get_tokenizer(model_name)
-
-# Generate descriptions
-def generate_description(item):
-    return f"A fashion item called {item}"
-
-items_desc = [generate_description(item) for item in items]
-text = tokenizer(items_desc)
-
-# Encode text features
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-model.to(device)
-
-torch.cuda.empty_cache()  # Avant de charger le modèle
-
-with torch.no_grad(), torch.amp.autocast(device_type=device):
-    text_features = model.encode_text(text.to(device))
-    text_features /= text_features.norm(dim=-1, keepdim=True)
-
-# Prediction function
-def predict(image, url):
-    if url:
-        response = requests.get(url)
-        image = Image.open(BytesIO(response.content))
-    
-    processed_image = preprocess_val(image).unsqueeze(0).to(device)
-
-    with torch.no_grad(), torch.amp.autocast(device_type=device):
-        image_features = model.encode_image(processed_image)
-        image_features /= image_features.norm(dim=-1, keepdim=True)
-
-        text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
-        
-        sorted_confidences = sorted(
-            {items[i]: float(text_probs[0, i]) for i in range(len(items))}.items(), 
-            key=lambda x: x[1], 
-            reverse=True
-        )
-        
-        # Include category IDs in the response
-        top_10_categories = [
-            {
-                "category_name": category["name"],
-                "id": category["id"],
-                "confidence": confidence
-            }
-            for category_name, confidence in sorted_confidences[:10]
-            for category in categories if category["name"] == category_name
-        ]
-        
-    return image, top_10_categories
-
-# Ajout de la fonction de prédiction par lots
-def predict_batch(images, urls):
-    # Combiner les images provenant des URLs et des téléchargements directs
-    combined_images = []
-    for image, url in zip(images, urls):
-        if url:
-            response = requests.get(url)
-            image = Image.open(BytesIO(response.content))
-        combined_images.append(preprocess_val(image).unsqueeze(0).to(device))
-    
-    # Empiler toutes les images traitées en un seul lot
-    batch_images = torch.cat(combined_images, dim=0)
-
-    with torch.no_grad(), torch.amp.autocast(device_type=device):
-        image_features = model.encode_image(batch_images)
-        image_features /= image_features.norm(dim=-1, keepdim=True)
-
-        text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
-        
-        # Traiter chaque image dans le lot
-        batch_results = []
-        for i in range(len(images)):
-            sorted_confidences = sorted(
-                {items[j]: float(text_probs[i, j]) for j in range(len(items))}.items(), 
-                key=lambda x: x[1], 
-                reverse=True
-            )
-            
-            # Inclure les IDs de catégorie dans la réponse
-            top_10_categories = [
-                {
-                    "category_name": category["name"],
-                    "id": category["id"],
-                    "confidence": confidence
-                }
-                for category_name, confidence in sorted_confidences[:10]
-                for category in categories if category["name"] == category_name
-            ]
-            batch_results.append(top_10_categories)
-        
-    return batch_results
-
-# Clear function
-def clear_fields():
-    # return None, "", None, ""
-    return None, ""
-# Gradio interface
-title = "Fashion Item Classifier with Marqo-FashionSigLIP"
-description = "Upload an image or provide a URL of a fashion item to classify it using [Marqo-FashionSigLIP](https://huggingface.co/Marqo/marqo-fashionSigLIP)!"
-
-examples = [
-    ["images/dress.jpg", "Dress"],
-    ["images/sweatpants.jpg", "Sweatpants"],
-    ["images/t-shirt.jpg", "T-Shirt"],
-]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown(f"# {title}")
-            gr.Markdown(description)
-            gr.Markdown(sidebar_markdown)
-        with gr.Column(scale=2):
-            input_image = gr.Image(type="pil", label="Upload Fashion Item Image", height=312)
-            input_url = gr.Textbox(label="Or provide an image URL")
-            # input_images = gr.Image(type="pil", label="Upload Fashion Item Images", height=312)
-            # input_urls = gr.Textbox(label="Or provide image URLs (comma-separated)", lines=2)
-            with gr.Row():
-                predict_button = gr.Button("Classify")
-                # predict_batch_button = gr.Button("Classify Batch")
-                clear_button = gr.Button("Clear")
-            gr.Markdown("Or click on one of the images below to classify it:")
-            gr.Examples(examples=examples, inputs=input_image)
-            output_label = gr.JSON(label="Top Categories")
-            # output_batch_label = gr.JSON(label="Top Categories for Each Image")
-            predict_button.click(predict, inputs=[input_image, input_url], outputs=[input_image, output_label])
-            # predict_batch_button.click(predict_batch, inputs=[input_images, input_urls], outputs=output_batch_label)
-            # clear_button.click(clear_fields, outputs=[input_image, input_url, input_images, input_urls])
-        
-# Launch the interface
-demo.launch()