update app

app.py

@@ -17,14 +17,36 @@ def load_data_hf():
     npy_url = "https://huggingface.co/datasets/traopia/vogue_runway_small/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),  mmap_mode="r")
+    embeddings = np.load(BytesIO(response.content))
 
     return df, embeddings
 
+
+from huggingface_hub import hf_hub_download
+def load_data1():
+# Login using e.g. `huggingface-cli login` to access this dataset
+    path = hf_hub_download(
+        repo_id="traopia/fashion_show_data_all_embeddings",
+        filename="fashion_show_data_all_embeddings.json"
+    )
+    df = pd.read_json(path, lines = True)
+
+    #df = pd.read_json("hf://datasets/traopia/fashion_show_data_all_embeddings.json/fashion_show_data_all_embeddings.json", lines=True)
+    df["fashion_clip_image"] = df["fashion_clip_image"].apply(lambda x: x[0] if isinstance(x, list) else x)
+    df["image_urls"] = df["image_urls"].apply(lambda x: x[0] if x is not None else None)
+    df = df.rename(columns={"fashion_house":"designer", "image_urls":"url", "URL":"collection"})
+    
+    df = df.dropna(subset="fashion_clip_image")
+    df = df.reset_index(drop=True)
+    df["key"] = df.index
+    embeddings = np.vstack(df["fashion_clip_image"].values)
+    
+    return df, embeddings
+
 df, embeddings = load_data_hf()
 
 # Filter and search
-def filter_and_search(fashion_house, category, season, year_range, query):
+def filter_and_search(fashion_house, category, season, start_year, end_year, query):
     filtered = df.copy()
 
     if fashion_house:
@@ -33,7 +55,7 @@ def filter_and_search(fashion_house, category, season, year_range, query):
         filtered = filtered[filtered['category'].isin(category)]
     if season:
         filtered = filtered[filtered['season'].isin(season)]
-    filtered = filtered[(filtered['year'] >= year_range[0]) & (filtered['year'] <= year_range[1])]
+    filtered = filtered[(filtered['year'] >= start_year) & (filtered['year'] <= end_year)]
 
     if query:
         results = search_images_by_text(query, filtered, embeddings)
@@ -68,14 +90,20 @@ with gr.Blocks() as demo:
         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)
-        year_range = gr.Slider(label="Year Range", minimum=int(df['year'].min()), maximum=int(df['year'].max()), value=(2000, 2025), step=1)
+        #year_range = gr.RangeSlider(label="Year Range", minimum=int(df['year'].min()), maximum=int(df['year'].max()), value=(2000, 2025), step=1)
+        
+        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=2025, step=1)
 
     query = gr.Textbox(label="Search", placeholder="e.g., pink dress")
     search_button = gr.Button("Search")
     
-    result_gallery = gr.Gallery(label="Search Results").style(grid=[5], height="auto")
+    result_gallery = gr.Gallery(label="Search Results", columns=5, height="auto")
     metadata_output = gr.Markdown()
-    similar_gallery = gr.Gallery(label="Similar Images").style(grid=[5], height="auto")
+    similar_gallery = gr.Gallery(label="Similar Images", columns = 5, height="auto")
 
     metadata_state = gr.State([])
     selected_idx = gr.Number(value=0, visible=False)
@@ -86,7 +114,7 @@ with gr.Blocks() as demo:
 
     search_button.click(
         handle_search,
-        inputs=[fashion_house, category, season, year_range, query],
+        inputs=[fashion_house, category, season, start_year, end_year, query],
         outputs=[result_gallery, metadata_state, metadata_output, similar_gallery]
     )