Issue fix

app.py

@@ -142,6 +142,13 @@ class SkinGPT4(nn.Module):
         self.P = 14  # Patch size
         self.D = 1408  # ViT embedding dimension
         self.num_query_tokens = 32
+
+        self.tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf",
+                                                        token=token, padding_side="right")
+
+        print("Loaded tokenizer")
+        self.tokenizer.add_special_tokens({'additional_special_tokens': ['<ImageHere>']})
+
         # Initialize components
         self.vit = self._init_vit(vit_checkpoint_path)
         print("Loaded ViT")
@@ -157,6 +164,8 @@ class SkinGPT4(nn.Module):
         self.q_former.eval()
         print("Loaded QFormer")
         self.llama = self._init_llama()
+        self.llama.resize_token_embeddings(len(self.tokenizer))
+
         self.llama_proj = nn.Linear(
             self.q_former.bert_config.hidden_size,
             self.llama.config.hidden_size
@@ -169,10 +178,7 @@ class SkinGPT4(nn.Module):
             torch.zeros(1, self.num_query_tokens, self.q_former.bert_config.hidden_size)
         )
         nn.init.normal_(self.query_tokens, std=0.02)
-        self.tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf",
-                                                   token=token, padding_side="right")
 
-        print("Loaded tokenizer")
     def _init_vit(self, vit_checkpoint_path):
         """Initialize EVA-ViT-G with paper specifications"""
         vit = create_eva_vit_g(
@@ -358,8 +364,8 @@ class SkinGPT4(nn.Module):
 
         # Tokenize prompt
 
-        self.tokenizer.add_special_tokens({'additional_special_tokens': ['<ImageHere>']})
-        self.llama.resize_token_embeddings(len(self.tokenizer))
+        # self.tokenizer.add_special_tokens({'additional_special_tokens': ['<ImageHere>']})
+        # self.llama.resize_token_embeddings(len(self.tokenizer))
 
         inputs = self.tokenizer(prompt, return_tensors="pt").to(images.device)
 
@@ -380,16 +386,16 @@ class SkinGPT4(nn.Module):
         return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 
-def load_model(model_path):
-    model_path = hf_hub_download(
-        repo_id="KeerthiVM/SkinCancerDiagnosis",
-        filename="dermnet_finetuned_version1.pth",
-    )
-    # model = SkinGPT4(vit_checkpoint_path="dermnet_finetuned_version1.pth")
-    model = SkinGPT4(vit_checkpoint_path=model_path)
-    model.to(device)
-    model.eval()
-    return model
+# def load_model(model_path):
+#     model_path = hf_hub_download(
+#         repo_id="KeerthiVM/SkinCancerDiagnosis",
+#         filename="dermnet_finetuned_version1.pth",
+#     )
+#     # model = SkinGPT4(vit_checkpoint_path="dermnet_finetuned_version1.pth")
+#     model = SkinGPT4(vit_checkpoint_path=model_path)
+#     model.to(device)
+#     model.eval()
+#     return model