revert

app.py

@@ -5,17 +5,26 @@ import torch
 import requests
 
 # ----------------------------
-# MODEL LOADING (MedVLM-R1)
+# MODEL LOADING (MedVLM-R1) - CPU Compatible
 # ----------------------------
 MODEL_PATH = 'JZPeterPan/MedVLM-R1'
 
+# Check if CUDA is available, otherwise use CPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
 model = Qwen2VLForConditionalGeneration.from_pretrained(
     MODEL_PATH,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
+    torch_dtype=torch.float32 if device == "cpu" else torch.bfloat16,
+    device_map="auto" if device == "cuda" else None,
+    low_cpu_mem_usage=True if device == "cpu" else False,
 )
 processor = AutoProcessor.from_pretrained(MODEL_PATH)
 
+# Move model to device
+if device == "cpu":
+    model = model.to(device)
+
 temp_generation_config = GenerationConfig(
     max_new_tokens=1024,
     do_sample=False,
@@ -71,7 +80,7 @@ def process_pipeline(image, user_question):
         videos=video_inputs,
         padding=True,
         return_tensors="pt",
-    ).to("cuda")
+    ).to(device)
 
     # Generate output from MedVLM
     generated_ids = model.generate(
@@ -153,4 +162,4 @@ with gr.Blocks(title="Brain MRI QA") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

requirements.txt

@@ -1,13 +1,15 @@
 gradio==5.42.0
-transformers>=4.40.0
-torch>=2.0.0
-torchvision>=0.15.0
+transformers==4.36.0
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==2.1.0+cpu
+torchvision==0.16.0+cpu
+torchaudio==2.1.0+cpu
 requests>=2.31.0
 Pillow>=10.0.0
 accelerate>=0.20.0
 safetensors>=0.3.0
 tokenizers>=0.15.0
-numpy>=1.24.0
+numpy<2.0.0
 scipy>=1.10.0
 qwen-vl-utils
 ipython>=8.0.0