Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration, BitsAndBytesConfig
+from peft import PeftModel
+import spaces
+import torch
+from huggingface_hub.hf_api import HfFolder
+import os
+token = os.getenv('token')
+HfFolder.save_token(token)
+device = "cuda"
+model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-mix-224")
+processor = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
+
+@spaces.GPU(duration=120)
+def greet(image, prompt):
+    
+    # quantization_config = BitsAndBytesConfig(
+    #     load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16
+    # )
+    
+    
+    # model = PaliGemmaForConditionalGeneration.from_pretrained("/folders", torch_dtype=torch.float16, quantization_config=quantization_config).to(device)
+    
+    
+    # # model = PeftModel(base_model, "/folders").to(device)
+    inputs = processor(prompt, raw_image, return_tensors="pt")
+    output = model.generate(**inputs, max_new_tokens=20)
+    return output
+
+demo = gr.Interface(fn=greet, inputs=[gr.Image(label="Upload image", sources=['upload', 'webcam'], type="pil"), gr.Text()], outputs="text")
+demo.launch()