Update app.py

app.py

@@ -95,6 +95,69 @@ def run_example(image, text_input=None, model_id="mateoluksenberg/Qwen-modelo-im
     output_text = processor.batch_decode(
         generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
     )
+
+    "---------------"
+    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)
+    "---------------"
     
     return output_text[0]