Update app.py

app.py

@@ -1,49 +1,18 @@
 import gradio as gr
 from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
 
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
-model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2"
+).to("cuda")
 
 def launch(input):
-    messages = [
-        {
-        "role": "user",
-        "content": 
-            [
-                {
-                "type": "image",
-                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
-                },
-                {
-                    "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
-    )
-    return(output_text)
+    out = model.generate(**input)
+    return(out)
 
 iface = gr.Interface(launch,
                      inputs=gr.Image(type='pil'),