f

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from PIL import Image
 import torch
-from transformers import AutoProcessor, AutoModelForCausalLM, BlipProcessor
+from transformers import AutoProcessor, BlipForConditionalGeneration, BlipProcessor
 import os
 
 # Check if we're running on CPU or GPU
@@ -22,21 +22,19 @@ except Exception as e:
 # Load base model - use the smallest possible model for CPU
 try:
     # Try loading the smallest BLIP model
-    model = AutoModelForCausalLM.from_pretrained(
+    model = BlipForConditionalGeneration.from_pretrained(
         "Salesforce/blip-image-captioning-base",
-        device_map=None,
         torch_dtype=torch.float32  # Use float32 for CPU compatibility
     )
     print("Loaded base BLIP model")
 except Exception as e:
     print(f"Error loading model: {e}")
-    # If that fails, load an even smaller model
-    model = AutoModelForCausalLM.from_pretrained(
+    # If that fails, load with low memory usage
+    model = BlipForConditionalGeneration.from_pretrained(
         "Salesforce/blip-image-captioning-base", 
-        device_map=None,
         low_cpu_mem_usage=True
     )
-    print("Loaded fallback model")
+    print("Loaded fallback model with low memory settings")
 
 # Move model to device if needed
 model = model.to(device)