fix added

SkinGPT.py

@@ -310,27 +310,26 @@ class SkinGPT4(nn.Module):
         print(f"\n[DEBUG] After replacement:")
         print(f"Image token embedding (after):\n{input_embeddings[0, replace_positions[0][1], :5]}...")
 
-        # outputs = self.llama.generate(
-        #     inputs_embeds=input_embeddings,
-        #     max_new_tokens=max_new_tokens,
-        #     temperature=0.7,
-        #     top_k=40,
-        #     top_p=0.9,
-        #     repetition_penalty=1.1,
-        #     do_sample=True,
-        #     pad_token_id = self.tokenizer.eos_token_id,
-        #     eos_token_id = self.tokenizer.eos_token_id
-        # )
-        #
-        #
-        # full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        # # print(f"Full Output from llama : {full_output}")
-        # response = full_output.split("### Response:")[-1].strip()
-        # # print(f"Response from llama : {full_output}")
-        #
-        # return response
-
-        return None
+        outputs = self.llama.generate(
+            inputs_embeds=input_embeddings,
+            max_new_tokens=max_new_tokens,
+            temperature=0.7,
+            top_k=40,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            do_sample=True,
+            pad_token_id = self.tokenizer.eos_token_id,
+            eos_token_id = self.tokenizer.eos_token_id
+        )
+
+
+        full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # print(f"Full Output from llama : {full_output}")
+        response = full_output.split("### Response:")[-1].strip()
+        # print(f"Response from llama : {full_output}")
+
+        return response
+
 
 class SkinGPTClassifier:
     def __init__(self, device='cuda' if torch.cuda.is_available() else 'cpu'):

app.py

@@ -49,6 +49,7 @@ warnings.filterwarnings("ignore")
 
 @st.cache_resource
 def get_classifier():
+    st.cache_resource.clear()
     torch.use_deterministic_algorithms(True)
     classifier = SkinGPTClassifier()
     for module in [classifier.model.vit,