commit25

app.py

@@ -486,13 +486,14 @@ def hybrid_scale(dense, sparse, alpha: float):
     return hdense, hsparse
 
 def extract_intent_from_openai(query: str):
-    prompt = f'''
+    prompt = f"""
 You are an assistant for a fashion search engine. Extract the user's intent from the following query.
 Return a Python dictionary with keys: category, gender, subcategory, color.
 If something is missing, use null.
+
 Query: "{query}"
 Only return the dictionary.
-'''
+"""
     try:
         response = openai.ChatCompletion.create(
             model="gpt-4",
@@ -504,26 +505,141 @@ Only return the dictionary.
         return structured
     except Exception as e:
         print(f"⚠️ OpenAI intent extraction failed: {e}")
-        return {}
-
-def is_duplicate(img, seen_hashes):
-    h = hash(img.tobytes())
-    if h in seen_hashes:
-        return True
-    seen_hashes.add(h)
+        return {"include": {}, "exclude": {}}
+#-----------------below changed------------------------------#
+
+import imagehash
+from PIL import Image
+
+def is_duplicate(img, existing_hashes, hash_size=16, tolerance=0):
+    """
+    Checks if the image is a near-duplicate based on perceptual hash.
+    :param img: PIL Image
+    :param existing_hashes: set of previously seen hashes
+    :param hash_size: size of the hash (default=16 for more precision)
+    :param tolerance: allowable Hamming distance for near-duplicates
+    :return: (bool) whether image is duplicate
+    """
+    img_hash = imagehash.phash(img, hash_size=hash_size)
+    for h in existing_hashes:
+        if abs(img_hash - h) <= tolerance:
+            return True
+    existing_hashes.add(img_hash)
     return False
 
+def extract_metadata_filters(query: str):
+    query_lower = query.lower()
+    gender = None
+    category = None
+    subcategory = None
+    color = None
+
+    # --- Gender Mapping ---
+    gender_map = {
+        "men": "Men", "man": "Men", "mens": "Men", "mans": "Men", "male": "Men",
+        "women": "Women", "woman": "Women", "womens": "Women", "female": "Women",
+        "boys": "Boys", "boy": "Boys",
+        "girls": "Girls", "girl": "Girls",
+        "kids": "Kids", "kid": "Kids",
+        "unisex": "Unisex"
+    }
+    for term, mapped_value in gender_map.items():
+        if term in query_lower:
+            gender = mapped_value
+            break
+
+    # --- Category Mapping ---
+    category_map = {
+        "shirt": "Shirts",
+        "tshirt": "Tshirts",
+        "t-shirt": "Tshirts",
+        "jeans": "Jeans",
+        "watch": "Watches",
+        "kurta": "Kurtas",
+        "dress": "Dresses",
+        "trousers": "Trousers", "pants": "Trousers",
+        "shorts": "Shorts",
+        "footwear": "Footwear",
+        "shoes": "Shoes",
+        "fashion": "Apparel"
+    }
+    for term, mapped_value in category_map.items():
+        if term in query_lower:
+            category = mapped_value
+            break
+
+    # --- SubCategory Mapping ---
+    subCategory_list = [
+        "Accessories", "Apparel Set", "Bags", "Bath and Body", "Beauty Accessories",
+        "Belts", "Bottomwear", "Cufflinks", "Dress", "Eyes", "Eyewear", "Flip Flops",
+        "Fragrance", "Free Gifts", "Gloves", "Hair", "Headwear", "Home Furnishing",
+        "Innerwear", "Jewellery", "Lips", "Loungewear and Nightwear", "Makeup",
+        "Mufflers", "Nails", "Perfumes", "Sandal", "Saree", "Scarves", "Shoe Accessories",
+        "Shoes", "Skin", "Skin Care", "Socks", "Sports Accessories", "Sports Equipment",
+        "Stoles", "Ties", "Topwear", "Umbrellas", "Vouchers", "Wallets", "Watches",
+        "Water Bottle", "Wristbands"
+    ]
+    if "topwear" in query_lower or "top" in query_lower:
+        subcategory = "Topwear"
+    else:
+        query_words = query_lower.split()
+        for subcat in subCategory_list:
+            if subcat.lower() in query_words:
+                subcategory = subcat
+                break
+
+    # --- Color Extraction ---
+    color_list = [
+        "red", "blue", "green", "yellow", "black", "white",
+        "orange", "pink", "purple", "brown", "grey", "beige"
+    ]
+    for c in color_list:
+        if c in query_lower:
+            color = c.capitalize()
+            break
+
+    # --- Invalid pairs ---
+    invalid_pairs = {
+        ("Men", "Dresses"), ("Men", "Sarees"), ("Men", "Skirts"),
+        ("Boys", "Dresses"), ("Boys", "Sarees"),
+        ("Girls", "Boxers"), ("Men", "Heels")
+    }
+    if (gender, category) in invalid_pairs:
+        print(f"⚠️ Invalid pair: {gender} + {category}, dropping gender")
+        gender = None
+
+    # --- Fallback for missing category ---
+    if gender and not category:
+        category = "Apparel"
+
+    # --- Refine subcategory for party/wedding-related queries ---
+    if "party" in query_lower or "wedding" in query_lower or "cocktail" in query_lower:
+        if subcategory in ["Loungewear and Nightwear", "Nightdress", "Innerwear"]:
+            subcategory = None  # reset it to avoid filtering into wrong items
+
+
+    return gender, category, subcategory, color
+
 # ------------------- Search Functions -------------------
 def search_fashion(query: str, alpha: float, start: int = 0, end: int = 12, gender_override: str = None):
     intent = extract_intent_from_openai(query)
-    gender = intent.get("gender")
-    category = intent.get("category")
-    subcategory = intent.get("subcategory")
-    color = intent.get("color")
+
+    include = intent.get("include", {})
+    exclude = intent.get("exclude", {})
+
+    gender = include.get("gender")
+    category = include.get("category")
+    subcategory = include.get("subcategory")
+    color = include.get("color")
+
+    # Apply override from dropdown
     if gender_override:
         gender = gender_override
 
+    # Build Pinecone filter
     filter = {}
+
+    # Inclusion filters
     if gender:
         filter["gender"] = gender
     if category:
@@ -533,9 +649,42 @@ def search_fashion(query: str, alpha: float, start: int = 0, end: int = 12, gend
             filter["articleType"] = category
     if subcategory:
         filter["subCategory"] = subcategory
+    
+    # Step 4: Exclude irrelevant items for party-like queries
+    query_lower = query.lower()
+    if any(word in query_lower for word in ["party", "wedding", "cocktail", "traditional", "reception"]):
+        filter.setdefault("subCategory", {})
+        if isinstance(filter["subCategory"], dict):
+            filter["subCategory"]["$nin"] = [
+                "Loungewear and Nightwear", "Nightdress", "Innerwear", "Sleepwear", "Vests", "Boxers"
+            ]
+
+
     if color:
         filter["baseColour"] = color
 
+    # Exclusion filters
+    exclude_filter = {}
+    if exclude.get("color"):
+        exclude_filter["baseColour"] = {"$ne": exclude["color"]}
+    if exclude.get("subcategory"):
+        exclude_filter["subCategory"] = {"$ne": exclude["subcategory"]}
+    if exclude.get("category"):
+        exclude_filter["articleType"] = {"$ne": exclude["category"]}
+
+    # Combine all filters
+    if filter and exclude_filter:
+        final_filter = {"$and": [filter, exclude_filter]}
+    elif filter:
+        final_filter = filter
+    elif exclude_filter:
+        final_filter = exclude_filter
+    else:
+        final_filter = None
+
+    print(f"🔍 Using filter: {final_filter} (showing {start} to {end})")
+
+    # Hybrid encoding
     sparse = bm25.encode_queries(query)
     dense = model.encode(query).tolist()
     hdense, hsparse = hybrid_scale(dense, sparse, alpha=alpha)
@@ -545,16 +694,25 @@ def search_fashion(query: str, alpha: float, start: int = 0, end: int = 12, gend
         vector=hdense,
         sparse_vector=hsparse,
         include_metadata=True,
-        filter=filter if filter else None
+        filter=final_filter
     )
 
+    # Retry fallback
     if len(result["matches"]) == 0:
         print("⚠️ No results, retrying with alpha=0 sparse only")
         hdense, hsparse = hybrid_scale(dense, sparse, alpha=0)
-        result = index.query(top_k=100, vector=hdense, sparse_vector=hsparse, include_metadata=True, filter=filter)
+        result = index.query(
+            top_k=100,
+            vector=hdense,
+            sparse_vector=hsparse,
+            include_metadata=True,
+            filter=final_filter
+        )
 
+    # Format results
     imgs_with_captions = []
     seen_hashes = set()
+
     for r in result["matches"]:
         idx = int(r["id"])
         img = images[idx]
@@ -563,179 +721,350 @@ def search_fashion(query: str, alpha: float, start: int = 0, end: int = 12, gend
             img = Image.fromarray(np.array(img))
         padded = ImageOps.pad(img, (256, 256), color="white")
         caption = str(meta.get("productDisplayName", "Unknown Product"))
+
         if not is_duplicate(padded, seen_hashes):
             imgs_with_captions.append((padded, caption))
+
         if len(imgs_with_captions) >= end:
             break
 
     return imgs_with_captions
 
 def search_by_image(uploaded_image, alpha=0.5, start=0, end=12):
+    # Step 1: Preprocess image for CLIP model
     processed = clip_processor(images=uploaded_image, return_tensors="pt").to(device)
+
     with torch.no_grad():
         image_vec = clip_model.get_image_features(**processed)
         image_vec = image_vec.cpu().numpy().flatten().tolist()
 
-    result = index.query(top_k=100, vector=image_vec, include_metadata=True)
+    # Step 2: Query Pinecone index for similar images
+    result = index.query(
+        top_k=100,  # fetch more to allow deduplication
+        vector=image_vec,
+        include_metadata=True
+    )
+
+    matches = result["matches"]
     imgs_with_captions = []
     seen_hashes = set()
 
-    for r in result["matches"]:
+    # Step 3: Deduplicate based on image hash
+    for r in matches:
         idx = int(r["id"])
         img = images[idx]
         meta = r.get("metadata", {})
         caption = str(meta.get("productDisplayName", "Unknown Product"))
+
         if not isinstance(img, Image.Image):
             img = Image.fromarray(np.array(img))
+
         padded = ImageOps.pad(img, (256, 256), color="white")
+
         if not is_duplicate(padded, seen_hashes):
             imgs_with_captions.append((padded, caption))
+
         if len(imgs_with_captions) >= end:
             break
 
     return imgs_with_captions
 
-# ------------------- UI -------------------
-# custom_css = """
-# .search-btn { width: 100%; }
-# .gr-row { gap: 8px !important; }
-# .query-slider > div { margin-bottom: 4px !important; }
-# .gr-gallery-item { width: 256px !important; height: 256px !important; }
-# .gr-gallery-item img { width: 100% !important; height: 100% !important; object-fit: cover !important; }
-# """
 
-# with gr.Blocks(css=custom_css) as demo:
-#     gr.Markdown("# 🛍️ Fashion Product Hybrid Search (with GPT-4 powered query parsing)")
+import gradio as gr
+import whisper
 
-#     with gr.Row(equal_height=True):
-#         with gr.Column(scale=5, elem_classes="query-slider"):
-#             query = gr.Textbox(label="Enter your fashion search query", placeholder="e.g., black sneakers for women")
-#             alpha = gr.Slider(0, 1, value=0.5, label="Hybrid Weight (alpha: 0=sparse, 1=dense)")
-#             gender_dropdown = gr.Dropdown(["", "Men", "Women", "Boys", "Girls", "Kids", "Unisex"], label="Gender Filter (optional)")
-#         with gr.Column(scale=1):
-#             image_input = gr.Image(type="pil", label="Upload an image (optional)", sources=["upload", "clipboard"], height=256, width=356)
+asr_model = whisper.load_model("base")
 
-#     search_btn = gr.Button("Search", elem_classes="search-btn")
-#     gallery = gr.Gallery(label="Search Results", columns=6, height=None)
-#     load_more_btn = gr.Button("Load More")
+def handle_voice_search(vf_path, a, offset, gender_ui):
+    try:
+        transcription = asr_model.transcribe(vf_path)["text"].strip()
+    except:
+        transcription = ""
+    filters = extract_intent_from_openai(transcription) if transcription else {}
+    gender_override = gender_ui if gender_ui else filters.get("gender")
+    results = search_fashion(transcription, a, 0, 12, gender_override)
+    seen_ids = {r[1] for r in results}
+    return results, 12, transcription, None, gender_override, results, seen_ids
 
-#     search_offset = gr.State(0)
-#     current_query = gr.State("")
-#     current_image = gr.State(None)
-#     current_gender = gr.State("")
-#     shown_results = gr.State([])
-#     shown_ids = gr.State(set())
+custom_css = """
+/* === Global Styling === */
+/* === Override Gradio default background === */
 
-#     def unified_search(q, uploaded_image, a, offset, gender_ui):
-#         start = 0
-#         end = 12
-#         filters = extract_intent_from_openai(q) if q.strip() else {}
-#         gender_override = gender_ui if gender_ui else filters.get("gender")
+html, body {
+    height: 100% !important;
+    margin: 0 !important;
+    padding: 0 !important;
+    background: radial-gradient(circle at center, #0b1f36 0%, #033e3e 100%) !important;
+    background-attachment: fixed;
+}
+
+.gr-root, .gr-block {
+    background: transparent !important;
+}
 
-#         if uploaded_image is not None:
-#             results = search_by_image(uploaded_image, a, start, end)
-#         elif q.strip():
-#             results = search_fashion(q, a, start, end, gender_override)
-#         else:
-#             results = []
 
-#         seen_ids = {r[1] for r in results}
-#         return results, end, q, uploaded_image, gender_override, results, seen_ids
+body::before {
+    content: "";
+    position: fixed;
+    top: 0; left: 0;
+    width: 100%; height: 100%;
+    background: radial-gradient(circle at center, rgba(0, 255, 255, 0.08), transparent);
+    z-index: -1;
+}
+#app-bg {
+    min-height: 100vh;
+    padding: 0;
+    margin: 0;
+    background: radial-gradient(circle at center, #0b1f36 0%, #033e3e 100%);
+    display: flex;
+    justify-content: center;
+    align-items: flex-start;
+    background-attachment: fixed;
+    position: relative;
+    overflow: hidden;
+}
 
-#     search_btn.click(unified_search, inputs=[query, image_input, alpha, search_offset, gender_dropdown],
-#                      outputs=[gallery, search_offset, current_query, current_image, current_gender, shown_results, shown_ids])
+#app-bg::before {
+    content: "";
+    position: absolute;
+    top: 0; left: 0;
+    width: 100%; height: 100%;
+    background: radial-gradient(circle at center, rgba(0, 255, 255, 0.08), transparent);
+    z-index: 0;
+}
 
-#     def load_more_fn(a, offset, q, img, gender_ui, prev_results, prev_ids):
-#         start = offset
-#         end = offset + 12
-#         gender_override = gender_ui
+#main-container {
+    z-index: 1;
+    position: relative;
+}
 
-#         if img is not None:
-#             new_results = search_by_image(img, a, start, end)
-#         elif q.strip():
-#             new_results = search_fashion(q, a, start, end, gender_override)
-#         else:
-#             new_results = []
 
-#         filtered_new = []
-#         new_ids = set()
-#         for item in new_results:
-#             img_obj, caption = item
-#             if caption not in prev_ids:
-#                 filtered_new.append(item)
-#                 new_ids.add(caption)
 
-#         combined = prev_results + filtered_new
-#         updated_ids = prev_ids.union(new_ids)
 
-#         return combined, end, combined, updated_ids
+/* === Heading Style === */
+h1, .gr-markdown h1 {
+    font-size: 2.2rem !important;
+    font-weight: bold;
+    color: #000000;
+    text-align: center;
+    margin-bottom: 1rem;
+}
+
+/* === Tabs === */
+.gr-tab {
+    border-radius: 12px !important;
+    background-color: #ffffff !important;
+    box-shadow: 0 3px 10px rgba(0, 0, 0, 0.08);
+    padding: 16px !important;
+    margin-top: 12px;
+}
+
+/* === Textbox, Dropdown, Slider === */
+input[type="text"], .gr-textbox textarea, .gr-dropdown, .gr-slider {
+    border-radius: 8px !important;
+    border: 1px solid #ccc !important;
+    padding: 10px !important;
+    font-size: 16px;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.05);
+}
+
+/* === Image Upload === */
+.gr-image {
+    width: 100% !important;
+    max-width: 100% !important;
+    border-radius: 12px;
+    box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+}
 
-#     load_more_btn.click(load_more_fn, inputs=[alpha, search_offset, current_query, current_image, current_gender, shown_results, shown_ids],
-#                         outputs=[gallery, search_offset, shown_results, shown_ids])
+/* === Buttons (custom style .button-36) === */
+.gr-button {
+  background-color: #DBDBDB !important;
+  background-image: linear-gradient(92.88deg, #455EB5 9.16%, #5643CC 43.89%, #673FD7 64.72%);
+  border-radius: 8px !important;
+  border-style: none !important;
+  box-sizing: border-box;
+  color: #FFFFFF !important;
+  cursor: pointer;
+  flex-shrink: 0;
+  font-family: "Inter UI","SF Pro Display",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Oxygen,Ubuntu,Cantarell,"Open Sans","Helvetica Neue",sans-serif;
+  font-size: 16px;
+  font-weight: 500;
+  height: 4rem;
+  padding: 0 1.6rem;
+  text-align: center;
+  text-shadow: rgba(0, 0, 0, 0.25) 0 3px 8px;
+  transition: all .5s;
+  user-select: none;
+  -webkit-user-select: none;
+  touch-action: manipulation;
+}
 
-#     gr.Markdown("🧠 Powered by OpenAI + Hybrid AI Fashion Search")
+.gr-button:hover {
+  box-shadow: rgba(80, 63, 205, 0.5) 0 1px 30px;
+  transition-duration: .1s;
+}
 
-# demo.launch()
-import gradio as gr
+/* === Responsive padding === */
+@media (min-width: 768px) {
+  .gr-button {
+    padding: 0 2.6rem;
+  }
+}
 
-custom_css = """
-/* Container */
+/* === Gallery Grid === */
 .gr-gallery {
-    display: flex !important;
-    flex-wrap: wrap;
-    gap: 10px;
-    justify-content: center;
+    padding-top: 12px;
 }
-
-/* Each item */
 .gr-gallery-item {
-    flex: 0 0 calc(16.66% - 10px);  /* 6 columns on desktop */
-    max-width: calc(16.66% - 10px);
-    height: 256px !important;
+    width: 128px !important;
+    height: 128px !important;
+    transition: transform 0.3s ease-in-out;
+    border-radius: 8px;
     overflow: hidden;
 }
-
-/* Image inside each item */
+.gr-gallery-item:hover {
+    transform: scale(1.06);
+    box-shadow: 0 3px 12px rgba(0,0,0,0.15);
+}
 .gr-gallery-item img {
+    object-fit: cover !important;
     width: 100% !important;
     height: 100% !important;
-    object-fit: cover !important;
+    border-radius: 8px;
 }
 
-/* For mobile: 3 columns */
-@media (max-width: 768px) {
-    .gr-gallery-item {
-        flex: 0 0 calc(33.33% - 10px);  /* 3 columns on mobile */
-        max-width: calc(33.33% - 10px);
-    }
+/* === Audio Upload === */
+.gr-audio {
+    width: 100% !important;
+    border-radius: 12px;
+    background-color: #fff !important;
+    box-shadow: 0 1px 5px rgba(0,0,0,0.1);
+}
+
+/* === Footer === */
+.gr-markdown:last-child {
+    text-align: center;
+    font-size: 14px;
+    color: #666;
+    padding-top: 1rem;
+}
+
+/* === Main Container Centered and Wide === */
+#main-container {
+    max-width: 90%;
+    width: 1100px;
+    margin: 40px auto !important;
+    padding: 24px;
+    background: #ffffff;
+    border-radius: 18px;
+    box-shadow: 0 10px 30px rgba(0,0,0,0.08);
+    border: 3px solid orange; /* Orange border */
+}
+
+
+
+/* === Tab Label Styling === */
+button[role="tab"] {
+    color: #000000 !important;         /* Default tab text color: black */
+    font-weight: 500;
+    transition: color 0.3s ease-in-out;
+    font-size: 16px;
+}
+
+/* Active tab title */
+button[role="tab"][aria-selected="true"] {
+    color: #f57c00 !important;         /* Active tab text color: orange */
+    font-weight: bold !important;
+}
+
+/* Hover effect on tab titles */
+button[role="tab"]:hover {
+    color: #f57c00 !important;  /* Orange on hover */
+    font-weight: 600;
+    cursor: pointer;
 }
+/* === Uniform Input Sizes for Text, Audio, Image === */
+.gr-textbox, .gr-audio, .gr-image {
+    max-width: 100% !important;
+    width: 100% !important;
+}
+
+.gr-audio, .gr-image {
+    max-width: 500px !important;
+    margin: 0 auto;
+}
+
+.gr-image {
+    height: 256px !important;
+}
+
 """
 
-with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown("## 🛍️ Responsive Fashion Product Search")
-
-    with gr.Row():
-        with gr.Column(scale=5, elem_classes="query-slider"):
-            query = gr.Textbox(label="Search", placeholder="e.g. black dress for women")
-            alpha = gr.Slider(0, 1, value=0.5, label="Hybrid Weight")
-            gender_dropdown = gr.Dropdown(["", "Men", "Women", "Unisex"], label="Gender (optional)")
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="pil", label="Upload Image", sources=["upload", "clipboard"], height=256)
-
-    search_btn = gr.Button("Search", elem_classes="search-btn")
-    gallery = gr.Gallery(label="Results", columns=6, height=None, allow_preview=True)
-    load_more_btn = gr.Button("Load More")
-
-    search_offset = gr.State(0)
-    current_query = gr.State("")
-    current_image = gr.State(None)
-    current_gender = gr.State("")
-    shown_results = gr.State([])
-    shown_ids = gr.State(set())
 
+
+
+with gr.Blocks(css=custom_css) as demo:
+  with gr.Column(elem_id="app-bg"):
+    with gr.Column(elem_id="main-container"):
+      gr.Markdown("# 🛍️ Fashion Product Hybrid Search")
+
+      alpha = gr.Slider(0, 1, value=0.5, label="Hybrid Weight (alpha: 0=sparse, 1=dense)")
+
+      with gr.Tabs():
+          with gr.Tab("Text Search"):
+              query = gr.Textbox(
+                  label="Text Query",
+                  placeholder="e.g., floral summer dress for women"
+              )
+              gender_dropdown = gr.Dropdown(
+                  ["", "Men", "Women", "Boys", "Girls", "Kids", "Unisex"],
+                  label="Gender Filter (optional)"
+              )
+              text_search_btn = gr.Button("Search by Text", elem_classes="search-btn")
+          with gr.Tab("🎙️ Voice Search"):
+            voice_input = gr.Audio(label="Speak Your Query", type="filepath")
+            voice_gender_dropdown = gr.Dropdown(["", "Men", "Women", "Boys", "Girls", "Kids", "Unisex"], label="Gender")
+            voice_search_btn = gr.Button("Search by Voice")
+
+
+          with gr.Tab("Image Search"):
+              # image_input = gr.Image(
+              #     type="pil",
+              #     label="Upload an image",
+              #     sources=["upload", "clipboard"],
+              #     height=256,
+              #     width=356
+              # )
+              image_input = gr.Image(
+                type="pil",
+                label="Upload an image",
+                sources=["upload", "clipboard"],
+                # tool=None,
+                height=400
+              )
+
+              image_gender_dropdown = gr.Dropdown(
+                  ["", "Men", "Women", "Boys", "Girls", "Kids", "Unisex"],
+                  label="Gender Filter (optional)"
+              )
+              image_search_btn = gr.Button("Search by Image", elem_classes="search-btn")
+
+      gallery = gr.Gallery(label="Search Results", columns=6, height=None)
+      load_more_btn = gr.Button("Load More")
+
+    # --- UI State Holders ---
+      search_offset = gr.State(0)
+      current_query = gr.State("")
+      current_image = gr.State(None)
+      current_gender = gr.State("")
+      shown_results = gr.State([])
+      shown_ids = gr.State(set())
+
+    # --- Unified Search Function ---
     def unified_search(q, uploaded_image, a, offset, gender_ui):
         start = 0
         end = 12
+
         filters = extract_intent_from_openai(q) if q.strip() else {}
         gender_override = gender_ui if gender_ui else filters.get("gender")
 
@@ -749,12 +1078,28 @@ with gr.Blocks(css=custom_css) as demo:
         seen_ids = {r[1] for r in results}
         return results, end, q, uploaded_image, gender_override, results, seen_ids
 
-    search_btn.click(
+    # Text Search
+    # Text Search
+    text_search_btn.click(
+      unified_search,
+      inputs=[query, gr.State(None), alpha, search_offset, gender_dropdown],
+      outputs=[gallery, search_offset, current_query, current_image, current_gender, shown_results, shown_ids]
+    )
+
+    voice_search_btn.click(
+    handle_voice_search,
+    inputs=[voice_input, alpha, search_offset, voice_gender_dropdown],
+    outputs=[gallery, search_offset, current_query, current_image, current_gender, shown_results, shown_ids]
+)
+
+    # Image Search
+    image_search_btn.click(
         unified_search,
-        inputs=[query, image_input, alpha, search_offset, gender_dropdown],
+        inputs=[gr.State(""), image_input, alpha, search_offset, image_gender_dropdown],
         outputs=[gallery, search_offset, current_query, current_image, current_gender, shown_results, shown_ids]
     )
 
+    # --- Load More Button ---
     def load_more_fn(a, offset, q, img, gender_ui, prev_results, prev_ids):
         start = offset
         end = offset + 12
@@ -786,7 +1131,7 @@ with gr.Blocks(css=custom_css) as demo:
         outputs=[gallery, search_offset, shown_results, shown_ids]
     )
 
-    gr.Markdown("🧠 Powered by OpenAI + Hybrid AI Fashion Search")
+    # gr.Markdown("🧠 Powered by OpenAI + Hybrid AI Fashion Search")
 
 demo.launch()
 

requirements.txt

@@ -1,4 +1,4 @@
-gradio
+gradio>=4.0.0
 openai
 sentence-transformers==2.6.1
 torch>=2.0.0
@@ -6,7 +6,9 @@ transformers==4.41.1
 datasets
 Pillow
 pinecone-client==3.2.2
+pinecone-text
 scikit-learn
 tqdm
 numpy
-pinecone-text
+imagehash
+whisper