fix added

app.py

@@ -1,34 +1,38 @@
-import streamlit as st
-import torchvision.transforms as transforms
 import torch
+import random
+import numpy as np
+
+torch.manual_seed(42)
+random.seed(42)
+np.random.seed(42)
+
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+if torch.cuda.is_available():
+    torch.use_deterministic_algorithms(True)
+    torch.cuda.manual_seed_all(42)
+
+import streamlit as st
 import io
-import os
 from fpdf import FPDF
 import nest_asyncio
 nest_asyncio.apply()
 device='cuda' if torch.cuda.is_available() else 'cpu'
 
+
 st.set_page_config(page_title="DermBOT", page_icon="🧬", layout="centered")
 
-import torch
-from torch import nn
-from torchvision import transforms
 from PIL import Image
-from transformers import LlamaForCausalLM, LlamaTokenizer, BertModel, BertConfig
-from eva_vit import create_eva_vit_g
-import requests
-from io import BytesIO
 import os
-from huggingface_hub import hf_hub_download
-from transformers import BitsAndBytesConfig
-from accelerate import init_empty_weights
-import warnings
 from transformers import logging
 
 import torch
-from torch.cuda.amp import autocast
 from SkinGPT import SkinGPTClassifier
-# Set default dtypes
+
+
+
+
 torch.set_default_dtype(torch.float32)  # Main computations in float32
 MODEL_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
 import warnings
@@ -43,9 +47,18 @@ import warnings
 warnings.filterwarnings("ignore")
 
 
-
+@st.cache_resource
 def get_classifier():
-    return SkinGPTClassifier()
+    torch.use_deterministic_algorithms(True)
+    classifier = SkinGPTClassifier()
+    for module in [classifier.model.vit,
+                   classifier.model.q_former,
+                   classifier.model.llama]:
+        module.eval()
+        for param in module.parameters():
+            param.requires_grad = False
+
+    return classifier
 
 classifier = get_classifier()