Update app.py

app.py

@@ -10,43 +10,44 @@ model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms(
 tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
 
 def predict(inp):
-    catgs = [
-      "Shirts",
-      "SetShirtsPants",
-      "SetJacketsPants",
-      "Pants",
-      "Jeans",
-      "JacketsCoats",
-      "Shoes",
-      "Underpants",
-      "Socks",
-      "Hats",
-      "Wallets",
-      "Bags",
-      "Scarfs",
-      "Parasols&Umbrellas",
-      "Necklaces",
-      "Towels&Robes",
-      "WallObjects",
-      "Rugs",
-      "Glassware",
-      "Mugs&Cups",
-      "OralCare"
-    ]
-    text = tokenizer(catgs)
-    image = preprocess_val(inp).unsqueeze(0)
+    # catgs = [
+    #   "Shirts",
+    #   "SetShirtsPants",
+    #   "SetJacketsPants",
+    #   "Pants",
+    #   "Jeans",
+    #   "JacketsCoats",
+    #   "Shoes",
+    #   "Underpants",
+    #   "Socks",
+    #   "Hats",
+    #   "Wallets",
+    #   "Bags",
+    #   "Scarfs",
+    #   "Parasols&Umbrellas",
+    #   "Necklaces",
+    #   "Towels&Robes",
+    #   "WallObjects",
+    #   "Rugs",
+    #   "Glassware",
+    #   "Mugs&Cups",
+    #   "OralCare"
+    # ]
+    # text = tokenizer(catgs)
+    # image = preprocess_val(inp).unsqueeze(0)
 
-    with torch.no_grad(), torch.cuda.amp.autocast():
-        image_features = model.encode_image(image)
-        image_features /= image_features.norm(dim=-1, keepdim=True) 
-        text_features = model.encode_text(text)
-        text_features /= text_features.norm(dim=-1, keepdim=True)
-        text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-    
-    max_prob_idx = np.argmax(text_probs)
-    pred_lbl = catgs[max_prob_idx]
-    pred_lbl_prob = text_probs[0, max_prob_idx].item()
+    # with torch.no_grad(), torch.cuda.amp.autocast():
+    #     image_features = model.encode_image(image)
+    #     image_features /= image_features.norm(dim=-1, keepdim=True) 
+    #     text_features = model.encode_text(text)
+    #     text_features /= text_features.norm(dim=-1, keepdim=True)
+    #     text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
     
+    # max_prob_idx = np.argmax(text_probs)
+    # pred_lbl = catgs[max_prob_idx]
+    # pred_lbl_prob = text_probs[0, max_prob_idx].item()
+
+    pred_lbl = "clothing"
     mw = ["men", "women", "boy", "girl"]
     catgs = [
         mw[0] + "s " + pred_lbl,
@@ -67,7 +68,8 @@ def predict(inp):
     max_prob_idx = np.argmax(text_probs)
     pred_lbl_f = mw[max_prob_idx]
     pred_lbl_prob_f = text_probs[0, max_prob_idx].item()
-    tlt = f"{pred_lbl} <{100.0 * pred_lbl_prob:.1f}%> , {pred_lbl_f} <{100.0 * pred_lbl_prob_f:.1f}%>"
+    # tlt = f"{pred_lbl} <{100.0 * pred_lbl_prob:.1f}%> , {pred_lbl_f} <{100.0 * pred_lbl_prob_f:.1f}%>"
+    tlt = f"{pred_lbl_f} <{100.0 * pred_lbl_prob_f:.1f}%>"
     return(tlt)
 
 gr.Interface(fn=predict,