Create app.py

app.py

@@ -0,0 +1,63 @@
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig, AutoTokenizer, Qwen2TokenizerFast
+from PIL import Image
+import torch
+import requests
+from accelerate import init_empty_weights
+
+
+USE_GPU = True
+
+device = torch.device("cuda" if USE_GPU and torch.cuda.is_available() else "cpu")
+
+processor = AutoProcessor.from_pretrained(
+    'allenai/MolmoE-1B-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto' if USE_GPU else None,
+    cache_dir="./models/molmo1"
+)
+with init_empty_weights():
+    model = AutoModelForCausalLM.from_pretrained(
+        'allenai/MolmoE-1B-0924',
+        trust_remote_code=True,
+        torch_dtype='auto',
+        device_map='auto' if USE_GPU else None,
+        cache_dir="./models/molmo1",
+        attn_implementation="eager"
+    )
+
+
+
+if not USE_GPU:
+    model.to(device)
+
+model.tie_weights()
+
+image_path = "./public/image.jpg"  # Replace with your image file path
+image = Image.open(image_path)
+image = image.convert("RGB")
+
+inputs = processor.process(
+    images=[image],
+    text="Extract text"
+)
+
+inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+print('STARTED')
+output = model.generate_from_batch(
+    inputs,
+    GenerationConfig(
+        max_new_tokens=2000,
+        # temperature=0.1,
+        # top_p=top_p,
+        stop_strings="<|endoftext|>"
+    ),
+    tokenizer=processor.tokenizer
+)
+
+# Only get generated tokens; decode them to text
+generated_tokens = output[0, inputs['input_ids'].size(1):]
+generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+print(generated_text)
+