Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import spaces
-from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor
-from qwen_vl_utils import process_vision_info
+from transformers import Idefics3ForConditionalGeneration, AutoProcessor
 import torch
 from PIL import Image
 from datetime import datetime
@@ -10,22 +9,24 @@ import os
 
 
 DESCRIPTION = """
-# Qwen2-VL-7B-trl-sft-ChartQA Demo
+# SmolVLM-trl-sft-ChartQA Demo
 
-This is a demo Space for a fine-tuned version of [Qwen2-VL-7B](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) trained using [ChatQA dataset](https://huggingface.co/datasets/HuggingFaceM4/ChartQA).
+This is a demo Space for a fine-tuned version of [SmolVLM](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) trained using [ChatQA dataset](https://huggingface.co/datasets/HuggingFaceM4/ChartQA).
 
-The corresponding model is located [here](https://huggingface.co/sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA).
+The corresponding model is located [here](https://huggingface.co/sergiopaniego/smolvlm-instruct-trl-sft-ChartQA).
 """
 
-model_id = "Qwen/Qwen2-VL-7B-Instruct" 
-model = Qwen2VLForConditionalGeneration.from_pretrained(
+model_id = "HuggingFaceTB/SmolVLM-Instruct"
+model = Idefics3ForConditionalGeneration.from_pretrained(
     model_id,
     device_map="auto",
     torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2",
 )
-adapter_path = "sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA"
+
+processor = AutoProcessor.from_pretrained(model_id)
+adapter_path = "sergiopaniego/smolvlm-instruct-trl-sft-ChartQA"
 model.load_adapter(adapter_path)
-processor = Qwen2VLProcessor.from_pretrained(model_id)
 
 def array_to_image_path(image_array):
     if image_array is None:
@@ -101,7 +102,7 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
-    with gr.Tab(label="Qwen2-VL-7B-trl-sft-ChartQA Input"):
+    with gr.Tab(label="SmolVLM-trl-sft-ChartQA Input"):
         with gr.Row():
             with gr.Column():
                 input_img = gr.Image(label="Input Picture")