|
|
import gradio as gr |
|
|
import pandas as pd |
|
|
import numpy as np |
|
|
from sentence_transformers import SentenceTransformer |
|
|
from datasets import load_dataset |
|
|
from sklearn.metrics.pairwise import cosine_similarity |
|
|
from PIL import Image |
|
|
|
|
|
|
|
|
print("โณ Loading Model and Data...") |
|
|
model = SentenceTransformer('clip-ViT-B-32') |
|
|
|
|
|
|
|
|
df = pd.read_parquet("ven_inventory.parquet") |
|
|
inventory_embeddings = np.stack(df['embedding'].values) |
|
|
|
|
|
|
|
|
dataset = load_dataset("detection-datasets/fashionpedia", split='train') |
|
|
subset = dataset.select(range(5050)) |
|
|
|
|
|
|
|
|
def recommend(text_query, image_query, input_mode): |
|
|
if input_mode == "Text": |
|
|
if not text_query: return None |
|
|
query_emb = model.encode([text_query]) |
|
|
else: |
|
|
if image_query is None: return None |
|
|
|
|
|
img = Image.fromarray(image_query).convert("RGB") |
|
|
query_emb = model.encode([img]) |
|
|
|
|
|
|
|
|
query_emb = query_emb / np.linalg.norm(query_emb) |
|
|
|
|
|
|
|
|
scores = cosine_similarity(query_emb, inventory_embeddings)[0] |
|
|
top_indices = np.argsort(scores)[::-1][:3] |
|
|
|
|
|
results = [] |
|
|
for idx in top_indices: |
|
|
actual_idx = int(idx) |
|
|
results.append(( |
|
|
subset[actual_idx]['image'], |
|
|
f"Match Score: {scores[actual_idx]:.2%} | Cluster: {df.iloc[actual_idx]['cluster']}" |
|
|
)) |
|
|
return results |
|
|
|
|
|
|
|
|
with gr.Blocks(theme=gr.themes.Soft()) as demo: |
|
|
gr.Markdown("# ๐ฟ Ven Community - Fashion Recommender") |
|
|
gr.Markdown("Search Ven's inventory by text or image.") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
input_mode = gr.Radio(["Text", "Image"], label="Input Type", value="Text") |
|
|
|
|
|
|
|
|
text_input = gr.Textbox(label="Description", placeholder="e.g., White sneakers", visible=True) |
|
|
image_input = gr.Image(label="Upload Image", visible=False) |
|
|
|
|
|
search_btn = gr.Button("Find Similar Items", variant="primary") |
|
|
|
|
|
with gr.Column(): |
|
|
output_gallery = gr.Gallery(label="Results", columns=3) |
|
|
|
|
|
|
|
|
def update_visibility(mode): |
|
|
if mode == "Text": |
|
|
return gr.update(visible=True), gr.update(visible=False) |
|
|
else: |
|
|
return gr.update(visible=False), gr.update(visible=True) |
|
|
|
|
|
input_mode.change(update_visibility, inputs=input_mode, outputs=[text_input, image_input]) |
|
|
|
|
|
|
|
|
search_btn.click( |
|
|
fn=recommend, |
|
|
inputs=[text_input, image_input, input_mode], |
|
|
outputs=output_gallery |
|
|
) |
|
|
|
|
|
demo.launch() |
