create app.py

gradio
open_clip-torch
torch # <--- Ensure this line is present
datasets
torchvision
Pillow
numpy
transformers # (if you are using any transformer models directly besides open_clip)

app.py

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+import torch
+import open_clip
+import gradio as gr
+from datasets import load_dataset
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+
+# Load the dataset (fashion product images dataset)
+dataset = load_dataset("ceyda/fashion-products-small", split="train")
+
+# Load CLIP model with correct unpacking and QuickGELU
+model = open_clip.create_model("ViT-B-32-quickgelu", pretrained="openai")
+
+# Corrected image transform function
+preprocess = open_clip.image_transform(model.visual.image_size, is_train=False)
+
+# Load tokenizer
+tokenizer = open_clip.get_tokenizer("ViT-B-32")
+
+# Move model to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Function to compute image embeddings
+def get_image_embedding(image):
+    image = preprocess(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+    return image_features / image_features.norm(dim=-1, keepdim=True)
+
+# Function to compute text embeddings
+def get_text_embedding(text):
+    text_inputs = tokenizer([text]).to(device)
+    with torch.no_grad():
+        text_features = model.encode_text(text_inputs)
+    return text_features / text_features.norm(dim=-1, keepdim=True)
+
+# Precompute embeddings for all images in the dataset
+image_embeddings = []
+image_paths = []
+for item in dataset.select(range(1000)):  # Limit to 100 images for speed
+    image = item["image"]
+    image_embeddings.append(get_image_embedding(image))
+    image_paths.append(image)
+
+# Stack all embeddings into a tensor
+image_embeddings = torch.cat(image_embeddings, dim=0)
+
+# Function to search for similar images based on text
+def search_similar_image(query_text):
+    text_embedding = get_text_embedding(query_text)
+    similarities = (image_embeddings @ text_embedding.T).squeeze(1).cpu().numpy()
+    
+    # Get top 20 matches
+    best_match_idxs = np.argsort(similarities)[-20:][::-1]
+
+    return [image_paths[i] for i in best_match_idxs]
+
+# Function to search for similar images based on an uploaded image
+def search_similar_by_image(uploaded_image):
+    query_embedding = get_image_embedding(uploaded_image)
+    similarities = (image_embeddings @ query_embedding.T).squeeze(1).cpu().numpy()
+
+    # Get top 20 matches
+    best_match_idxs = np.argsort(similarities)[-20:][::-1]
+
+    return [image_paths[i] for i in best_match_idxs]
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🛍️ Visual Search for Fashion Products")
+    gr.Markdown("Search using **text** or **upload an image** to find similar items.")
+
+    with gr.Row():
+        query_input = gr.Textbox(label="Search by Text", placeholder="e.g., red sneakers")
+        search_button = gr.Button("Search by Text")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload an Image")
+        image_search_button = gr.Button("Search by Image")
+
+    output_gallery = gr.Gallery(label="Similar Items", columns=4, height=500)
+
+    search_button.click(search_similar_image, inputs=[query_input], outputs=[output_gallery])
+    image_search_button.click(search_similar_by_image, inputs=[image_input], outputs=[output_gallery])
+
+demo.launch(share=True)