Update app.py

app.py

@@ -97,65 +97,7 @@ def run_example(image, text_input=None, model_id="mateoluksenberg/Qwen-modelo-im
     )
 
     "---------------"
-    from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-    from qwen_vl_utils import process_vision_info
-    
-    # default: Load the model on the available device(s)
-    model = Qwen2VLForConditionalGeneration.from_pretrained(
-        "mateoluksenberg/Qwen-modelo-image", torch_dtype="auto", device_map="auto"
-    )
-    
-    # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-    # model = Qwen2VLForConditionalGeneration.from_pretrained(
-    #     "Qwen/Qwen2-VL-2B-Instruct",
-    #     torch_dtype=torch.bfloat16,
-    #     attn_implementation="flash_attention_2",
-    #     device_map="auto",
-    # )
-    
-    # default processer
-    processor = AutoProcessor.from_pretrained("mateoluksenberg/Qwen-modelo-image")
-    
-    # The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
-    # min_pixels = 256*28*28
-    # max_pixels = 1280*28*28
-    # processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-    
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "image",
-                    "image": image_path,
-                },
-                {"type": "text", "text": "Describe this image."},
-            ],
-        }
-    ]
-    
-    # Preparation for inference
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to("cuda")
-    
-    # Inference: Generation of the output
-    generated_ids = model.generate(**inputs, max_new_tokens=128)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
+
     print(output_text)
     "---------------"