rename to app.py

app.py

@@ -43,26 +43,7 @@ def load_data_hf():
     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()
 
@@ -107,108 +88,133 @@ def find_similar(idx, metadata):
 
 
 
-# Gradio UI
+
 with gr.Blocks() as demo:
     gr.Markdown("# 👗 FashionDB Explorer")
 
-    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="(optional): e.g., pink dress ")
-    search_button = gr.Button("Search by text")
-
-    uploaded_image = gr.Image(label="Upload an image", type="pil")  # or type="pil" if you prefer PIL Image object
-    search_by_image_button = gr.Button("Search by Image")
-
-    def handle_search_by_image(uploaded_image):
-        if uploaded_image is None:
-            return [], "Please upload an image first."
-        results_df = search_images_by_image(uploaded_image, df, embeddings)
-        # Convert results DataFrame to image URLs (or paths) for gallery display
-        images = results_df['url'].tolist()
-        metadata = results_df.to_dict(orient='records')
-        return images, metadata, ""
-
-    uploaded_metadata_state = gr.State([])
-    uploaded_metadata_output = gr.Markdown()
-    uploaded_result_gallery = gr.Gallery(label="Search Results by Image", columns=5, height="auto")
-
-    search_by_image_button.click(
-        fn=handle_search_by_image,
-        inputs=[uploaded_image],
-        outputs=[uploaded_result_gallery, uploaded_metadata_state, uploaded_metadata_output]
-    )
-    
-    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(*args):
-        imgs, meta = filter_and_search(*args)
-        return imgs, meta, "", []
-
-    search_button.click(
-        handle_search,
-        inputs=[fashion_house, category, season, start_year, end_year, query],
-        outputs=[result_gallery, metadata_state, metadata_output, similar_gallery]
-    )
-
-
-    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 [],[]  # safe fallback
-        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]
-    )
+    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]
+            )
 
     back_button = gr.Button("Back to Home")
 

app_twotabs.py → app_onetab.py

@@ -43,7 +43,26 @@ def load_data_hf():
     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()
 
@@ -88,133 +107,108 @@ def find_similar(idx, metadata):
 
 
 
-
+# Gradio UI
 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]
-            )
+    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="(optional): e.g., pink dress ")
+    search_button = gr.Button("Search by text")
+
+    uploaded_image = gr.Image(label="Upload an image", type="pil")  # or type="pil" if you prefer PIL Image object
+    search_by_image_button = gr.Button("Search by Image")
+
+    def handle_search_by_image(uploaded_image):
+        if uploaded_image is None:
+            return [], "Please upload an image first."
+        results_df = search_images_by_image(uploaded_image, df, embeddings)
+        # Convert results DataFrame to image URLs (or paths) for gallery display
+        images = results_df['url'].tolist()
+        metadata = results_df.to_dict(orient='records')
+        return images, metadata, ""
+
+    uploaded_metadata_state = gr.State([])
+    uploaded_metadata_output = gr.Markdown()
+    uploaded_result_gallery = gr.Gallery(label="Search Results by Image", columns=5, height="auto")
+
+    search_by_image_button.click(
+        fn=handle_search_by_image,
+        inputs=[uploaded_image],
+        outputs=[uploaded_result_gallery, uploaded_metadata_state, uploaded_metadata_output]
+    )
+    
+    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(*args):
+        imgs, meta = filter_and_search(*args)
+        return imgs, meta, "", []
+
+    search_button.click(
+        handle_search,
+        inputs=[fashion_house, category, season, start_year, end_year, query],
+        outputs=[result_gallery, metadata_state, metadata_output, similar_gallery]
+    )
+
+
+    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 [],[]  # safe fallback
+        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]
+    )
 
     back_button = gr.Button("Back to Home")