import gradio as gr import pandas as pd import numpy as np from search import search_images_by_text, get_similar_images, search_images_by_image import requests from io import BytesIO def create_collection_url(row): base_url = "https://www.vogue.com/fashion-shows/" season = str(row["season"]).lower() year = str(row["year"]) category = str(row["category"]).lower() if pd.notna(row["category"]) and row["category"] and str(row["category"]).lower() != "nan" else None designer = str(row["designer"]).lower().replace(" ", "-") # Add city if available city = str(row["city"]).lower().replace(" ", "-") if pd.notna(row["city"]) and row["city"] and str(row["city"]).lower() != "nan" else None if pd.isna(category) or category is None or category == "nan": if city: return f"{base_url}{city}-{season}-{year}/{designer}" else: return f"{base_url}{season}-{year}/{designer}" else: if city: return f"{base_url}{city}-{season}-{year}-{category}/{designer}" else: return f"{base_url}{season}-{year}-{category}/{designer}" import requests from io import BytesIO #@st.cache_data(show_spinner="Loading FashionDB...") def load_data_hf(): # Load the Parquet file directly from Hugging Face df_url = "https://huggingface.co/datasets/traopia/vogue-runway/resolve/main/VogueRunway.parquet" df = pd.read_parquet(df_url) # Load the .npy file using requests npy_url = "https://huggingface.co/datasets/traopia/vogue-runway/resolve/main/VogueRunway_image.npy" response = requests.get(npy_url) response.raise_for_status() # Raise error if download fails embeddings = np.load(BytesIO(response.content)) df['collection'] = df.apply(create_collection_url, axis=1) return df, embeddings df, embeddings = load_data_hf() # Filter and search def filter_and_search(fashion_house, category, season, start_year, end_year, query): filtered = df.copy() if fashion_house: filtered = filtered[filtered['designer'].isin(fashion_house)] if category: filtered = filtered[filtered['category'].isin(category)] if season: filtered = filtered[filtered['season'].isin(season)] filtered = filtered[(filtered['year'] >= start_year) & (filtered['year'] <= end_year)] if query: results = search_images_by_text(query, filtered, embeddings) else: results = filtered.head(30) image_urls = results["url"].tolist() metadata = results.to_dict(orient="records") return image_urls, metadata # Display metadata and similar def show_metadata(idx, metadata): item = metadata[idx] out = "" for field in ["designer", "season", "year", "category"]: if field in item and pd.notna(item[field]): out += f"**{field.title()}**: {item[field]}\n" if 'collection' in item and pd.notna(item['collection']): out += f"\n[View Collection]({item['collection']})" return out def find_similar(idx, metadata): if not isinstance(idx, int) or idx >= len(metadata) or idx < 0: return [] # or gr.update(visible=False) key = metadata[idx]["key"] similar_df = get_similar_images(df, key, embeddings, top_k=5) return similar_df["url"].tolist(), similar_df.to_dict(orient="records") with gr.Blocks() as demo: gr.Markdown("# 👗 FashionDB Explorer") with gr.Tabs(): # TEXT SEARCH TAB with gr.Tab("Search by Text"): with gr.Row(): fashion_house = gr.Dropdown(label="Fashion House", choices=sorted(df["designer"].dropna().unique()), multiselect=True) category = gr.Dropdown(label="Category", choices=sorted(df["category"].dropna().unique()), multiselect=True) season = gr.Dropdown(label="Season", choices=sorted(df["season"].dropna().unique()), multiselect=True) min_year = int(df['year'].min()) max_year = int(df['year'].max()) start_year = gr.Slider(label="Start Year", minimum=min_year, maximum=max_year, value=2000, step=1) end_year = gr.Slider(label="End Year", minimum=min_year, maximum=max_year, value=2024, step=1) query = gr.Textbox(label="Search by text", placeholder="e.g., pink dress") search_button = gr.Button("Search") result_gallery = gr.Gallery(label="Search Results", columns=5, height="auto") metadata_output = gr.Markdown() reference_image = gr.Image(label="Reference Image", interactive=False) similar_gallery = gr.Gallery(label="Similar Images", columns=5, height="auto") metadata_state = gr.State([]) selected_idx = gr.Number(value=0, visible=False) def handle_search(fh, cat, sea, sy, ey, q): imgs, meta = filter_and_search(fh, cat, sea, sy, ey, q) return imgs, meta, "", [], None search_button.click( handle_search, inputs=[fashion_house, category, season, start_year, end_year, query], outputs=[result_gallery, metadata_state, metadata_output, similar_gallery, reference_image] ) def handle_click(evt: gr.SelectData, metadata): idx = evt.index md = show_metadata(idx, metadata) img_path = metadata[idx]["url"] return idx, md, img_path result_gallery.select( handle_click, inputs=[metadata_state], outputs=[selected_idx, metadata_output, reference_image] ) def show_similar(idx, metadata): if idx is None or not str(idx).isdigit(): return [], [] return find_similar(int(idx), metadata) similar_metadata_state = gr.State() similar_metadata_output = gr.Markdown() show_similar_button = gr.Button("Show Similar Images") show_similar_button.click( show_similar, inputs=[selected_idx, metadata_state], outputs=[similar_gallery, similar_metadata_state] ) def handle_similar_click(evt: gr.SelectData, metadata): idx = evt.index md = show_metadata(idx, metadata) img_path = metadata[idx]["url"] return idx, md, img_path similar_gallery.select( handle_similar_click, inputs=[similar_metadata_state], outputs=[selected_idx, similar_metadata_output, reference_image] ) # IMAGE SEARCH TAB with gr.Tab("Search by Image"): with gr.Row(): fashion_house_img = gr.Dropdown(label="Fashion House", choices=sorted(df["designer"].dropna().unique()), multiselect=True) category_img = gr.Dropdown(label="Category", choices=sorted(df["category"].dropna().unique()), multiselect=True) season_img = gr.Dropdown(label="Season", choices=sorted(df["season"].dropna().unique()), multiselect=True) start_year_img = gr.Slider(label="Start Year", minimum=min_year, maximum=max_year, value=2000, step=1) end_year_img = gr.Slider(label="End Year", minimum=min_year, maximum=max_year, value=2024, step=1) uploaded_image = gr.Image(label="Upload an image", type="pil") search_by_image_button = gr.Button("Search by Image") uploaded_result_gallery = gr.Gallery(label="Search Results by Image", columns=5, height="auto") uploaded_metadata_state = gr.State([]) uploaded_metadata_output = gr.Markdown() uploaded_reference_image = gr.Image(label="Reference Image", interactive=False) def handle_search_by_image(image, fh, cat, sea, sy, ey): if image is None: return [], "Please upload an image first.", None # Apply filters filtered_df = df.copy() if fh: filtered_df = filtered_df[filtered_df["designer"].isin(fh)] if cat: filtered_df = filtered_df[filtered_df["category"].isin(cat)] if sea: filtered_df = filtered_df[filtered_df["season"].isin(sea)] filtered_df = filtered_df[(filtered_df["year"] >= sy) & (filtered_df["year"] <= ey)] results_df = search_images_by_image(image, filtered_df, embeddings) images = results_df['url'].tolist() metadata = results_df.to_dict(orient="records") return images, metadata, "" search_by_image_button.click( handle_search_by_image, inputs=[uploaded_image, fashion_house_img, category_img, season_img, start_year_img, end_year_img], outputs=[uploaded_result_gallery, uploaded_metadata_state, uploaded_metadata_output] ) uploaded_selected_idx = gr.Number(visible=False) def handle_uploaded_click(evt: gr.SelectData, metadata): idx = evt.index md = show_metadata(idx, metadata) img_path = metadata[idx]["url"] return idx, md, img_path uploaded_result_gallery.select( handle_uploaded_click, inputs=[uploaded_metadata_state], outputs=[uploaded_selected_idx, uploaded_metadata_output, uploaded_reference_image] ) # SIMILAR IMAGE SEARCH FOR IMAGE TAB uploaded_similar_gallery = gr.Gallery(label="Similar Images", columns=5, height="auto") uploaded_similar_metadata_state = gr.State([]) uploaded_similar_metadata_output = gr.Markdown() uploaded_show_similar_button = gr.Button("Show Similar Images") def show_similar_uploaded(idx, metadata): if idx is None or not str(idx).isdigit(): return [], [] return find_similar(int(idx), metadata) uploaded_show_similar_button.click( show_similar_uploaded, inputs=[uploaded_selected_idx, uploaded_metadata_state], outputs=[uploaded_similar_gallery, uploaded_similar_metadata_state] ) def handle_uploaded_similar_click(evt: gr.SelectData, metadata): idx = evt.index md = show_metadata(idx, metadata) img_path = metadata[idx]["url"] return idx, md, img_path uploaded_similar_gallery.select( handle_uploaded_similar_click, inputs=[uploaded_similar_metadata_state], outputs=[uploaded_selected_idx, uploaded_similar_metadata_output, uploaded_reference_image] ) uploaded_back_button = gr.Button("Back to Initial Uploaded Search") def back_to_uploaded_home(): return [], "", None uploaded_back_button.click( back_to_uploaded_home, outputs=[uploaded_similar_gallery, uploaded_similar_metadata_output, uploaded_reference_image] ) with gr.Tab("Query on FashionDB"): with gr.Row(): gr.Markdown( "### 🔗 Query FashionDB SPARQL Endpoint\n" "[Click here to open the SPARQL endpoint](https://fashionwiki.wikibase.cloud/query/)", elem_id="sparql-link" ) back_button = gr.Button("Back to Home") def back_to_home(): return [], "", None # clear similar_gallery, metadata_output, reference image back_button.click( back_to_home, outputs=[similar_gallery, similar_metadata_output, reference_image] ) demo.launch()