Update app.py

app.py

@@ -9,13 +9,13 @@ import gradio as gr
 import random
 import numpy as np
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
 
 def set_seed(seed_value=42):
     random.seed(seed_value)
     np.random.seed(seed_value)
     torch.manual_seed(seed_value)
-    torch.cuda.manual_seed_all(seed_value)
+    #torch.cuda.manual_seed_all(seed_value)
     
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
@@ -28,7 +28,7 @@ model, tokenizer = FastVisionModel.from_pretrained(
     use_gradient_checkpointing = "unsloth",
 )
 
-FastVisionModel.for_inference(model)
+#FastVisionModel.for_inference(model)
 
 instruction = "You are an expert radiographer. Describe accurately what you see in this image."
 
@@ -46,7 +46,7 @@ def predict_radiology_description(image, temperature, use_top_p, top_p_value, us
             input_text,
             add_special_tokens=False,
             return_tensors="pt",
-        ).to("cuda")
+        ).to(device)
 
         text_streamer = TextStreamer(tokenizer, skip_prompt=True)