Update app.py

app.py

@@ -143,20 +143,23 @@ def pil_to_base64(img, max_size=280):
     return base64.b64encode(buf.getvalue()).decode("utf-8")
 
 # ---------------------------------------------------------------------------
-# EMBEDDING + FAISS SEARCH (תיקון סופי ומאובטח לחילוץ הוקטורים מחבילת CLIP)
+# EMBEDDING + FAISS SEARCH (תיקון ה-TypeError וה-AttributeError לחילוץ וקטורים)
 # ---------------------------------------------------------------------------
 @torch.no_grad()
 def embed_query_image(pil_image):
     inputs = win_processor(images=pil_image, return_tensors="pt").to(DEVICE)
-    outputs = win_model.get_image_features(**inputs)
-    # חילוץ בטוח באמצעות בדיקת סוג האובייקט והפיכתו לטנסור טהור
-    if hasattr(outputs, "image_embeds"):
+    outputs = win_model(**inputs)
+    
+    # שימוש ישיר ובטוח בחילוץ הוקטור ללא תלות בסוג העטיפה של BaseModelOutput
+    if hasattr(outputs, "image_embeds") and outputs.image_embeds is not None:
         feats = outputs.image_embeds
-    elif isinstance(outputs, getattr(gr, "__package__", object)) or not hasattr(outputs, "cpu"):
-        feats = outputs[0] if (isinstance(outputs, tuple) or isinstance(outputs, list)) else outputs
+    elif hasattr(outputs, "pooler_output") and outputs.pooler_output is not None:
+        feats = outputs.pooler_output
+    elif isinstance(outputs, tuple) or isinstance(outputs, list):
+        feats = outputs[0]
     else:
         feats = outputs
-    
+
     if hasattr(feats, "detach"):
         feats = feats.detach()
     return feats.cpu().numpy().astype("float32")
@@ -164,12 +167,15 @@ def embed_query_image(pil_image):
 @torch.no_grad()
 def embed_query_text(sentence):
     inputs = win_processor(text=[sentence], return_tensors="pt", padding=True, truncation=True).to(DEVICE)
-    outputs = win_model.get_text_features(**inputs)
-    # חילוץ בטוח באמצעות בדיקת סוג האובייקט והפיכתו לטנסור טהור
-    if hasattr(outputs, "text_embeds"):
+    outputs = win_model(**inputs)
+    
+    # שימוש ישיר ובטוח בחילוץ הוקטור ללא תלות בסוג העטיפה של BaseModelOutput
+    if hasattr(outputs, "text_embeds") and outputs.text_embeds is not None:
         feats = outputs.text_embeds
-    elif isinstance(outputs, getattr(gr, "__package__", object)) or not hasattr(outputs, "cpu"):
-        feats = outputs[0] if (isinstance(outputs, tuple) or isinstance(outputs, list)) else outputs
+    elif hasattr(outputs, "pooler_output") and outputs.pooler_output is not None:
+        feats = outputs.pooler_output
+    elif isinstance(outputs, tuple) or isinstance(outputs, list):
+        feats = outputs[0]
     else:
         feats = outputs
         
@@ -193,7 +199,7 @@ def faiss_filtered_search(query_emb, top_k=3, exclude_idx=None, gender=None, age
             if idx == -1 or (exclude_idx is not None and idx == exclude_idx):
                 continue
             row = df.iloc[idx]
-            if require_gender and gender and str(row["gender"]).lower() != str(gender).lower():
+            if require_gender && gender and str(row["gender"]).lower() != str(gender).lower():
                 continue
             if require_age and age_group and str(row["age_group"]).lower() != str(age_group).lower():
                 continue
@@ -256,7 +262,6 @@ def generate_new_outfit_image(pil_image, row, target_gender=None):
     resized_protect_img = Image.fromarray(protect.astype(np.uint8) * 255).resize((head_w, head_h), resample=Image.NEAREST)
     
     canvas = Image.new("RGB", (canvas_w, canvas_h), color=(240, 238, 235))
-    # תיקון קריטי: הגדרת משתני המיקום המקומיים לחישוב הקנבס
     paste_x = (canvas_w - head_w) // 2
     paste_y = int(canvas_h * 0.03)
     canvas.paste(resized_face_crop, (paste_x, paste_y))
@@ -292,18 +297,6 @@ def build_style_card_html(row, caption):
     html += f'</div>'
     return html
 
-def to_shop_link(retailer, value, gender=None):
-    g = str(gender).strip().lower() if gender is not None else ""
-    dept = "men" if g in ("male", "man", "men", "m") else "women"
-    query_enc = urllib.parse.quote(str(value))
-    if retailer == "zara":
-        return f"https://www.zara.com/us/en/search?searchTerm={query_enc}"
-    if retailer == "asos":
-        return f"https://www.asos.com/us/search/?q={query_enc}"
-    if retailer == "hm":
-        return f"https://www2.hm.com/en_us/search-results.html?q={query_enc}"
-    return f"https://www.google.com/search?q={retailer}+{query_enc}"
-
 def build_outfit_component_cards_html(row):
     colors = [c.strip() for c in str(row["recommended_colors"]).split(",") if c.strip()] or ["neutral"]
     components = [
@@ -423,6 +416,15 @@ h1, h2, h3, p, span, label, input, select, textarea, button { color: #2C2A29 !im
 .dark-panel { background: #FFFFFF !important; border-radius: 16px !important; padding: 24px !important; border: 1px solid #ECE4D6 !important; margin-bottom: 20px; }
 .dark-panel label, .dark-panel span { color: #2C2A29 !important; font-weight: 600; }
 
+input, select, .secondary, .wrap, .slots, .single-select, .select-wrap { 
+    color: #2C2A29 !important; 
+    background-color: #FFFFFF !important;
+    border: 1px solid #E3DFDA !important;
+    border-radius: 4px !important;
+}
+div.form { background: transparent !important; border: none !important; box-shadow: none !important; }
+fieldset { display: flex !important; justify-content: center !important; gap: 24px !important; border: none !important; background: transparent !important; }
+
 .palette-premium-banner { background: #FAF3ED; padding: 24px; border-radius: 12px; margin-bottom: 20px; border-left: 5px solid #C69E6E; }
 .section-split { display: grid !important; grid-template-columns: repeat(2, 1fr) !important; gap: 20px !important; margin-top: 20px !important; width: 100% !important; }
 @media (max-width: 768px) { .section-split { grid-template-columns: 1fr !important; } }
@@ -446,7 +448,6 @@ h1, h2, h3, p, span, label, input, select, textarea, button { color: #2C2A29 !im
 .prod-title { font-size: 15px; font-weight: 700; color: #111111 !important; margin: 0 0 4px 0; line-height: 1.3; min-height: 40px; display: flex; align-items: center; }
 .prod-brand { font-size: 13px; color: #999999 !important; margin-bottom: 14px; display: block; }
 
-/* כפתורי הרכישה המקוריים */
 .shop-btn { display: block; width: 100%; background: #161617; color: #FFFFFF !important; text-align: center; padding: 11px 0; border-radius: 8px; font-size: 13px; font-weight: 700; text-decoration: none !important; letter-spacing: 0.5px; }
 .shop-btn:hover { background: #2D2D2F; color: #FFFFFF !important; }
 .more-matches-label { font-size: 11px; font-weight: 700; color: #9C8E82 !important; letter-spacing: 1.6px; text-transform: uppercase; margin: 24px 0 12px; }
@@ -487,7 +488,7 @@ with gr.Blocks(title="Personal Color Styling") as demo:
             [style_card_a, new_img_a, answer_a, outfit_cards_a],
         )
 
-        # תיקון: הגדרת מערך ערכים מלא ומדויק לכל הפרמטרים של ה-Examples כדי למנוע את שגיאת ה-load_example
+        # תיקון: הגדרת cache_examples=False כדי למנוע קריסות והדמיית קלט כפויה
         gr.Examples(
             examples=[
                 [SAMPLE_PHOTOS[0], "woman", "adult", "What style would suit me best?"],
@@ -497,7 +498,7 @@ with gr.Blocks(title="Personal Color Styling") as demo:
             inputs=[photo_in, gender_a, age_a, question_a],
             outputs=[style_card_a, new_img_a, answer_a, outfit_cards_a],
             fn=recommend_from_photo,
-            cache_examples=True,
+            cache_examples=False,
             label="Quick Starters",
         )
 
@@ -530,5 +531,4 @@ with gr.Blocks(title="Personal Color Styling") as demo:
         )
 
 if __name__ == "__main__":
-    # העברת פרמטרי ה-CSS וה-Theme באופן בטוח לתוך פונקציית הריצה הסופית כנדרש בגרדיו 6
     demo.launch(css=CUSTOM_CSS, theme=gr.themes.Soft(primary_hue="amber"))