Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+import spaces
+
+
+from PIL import Image 
+
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+model = AutoModelForCausalLM.from_pretrained('Ascetu/yungen', trust_remote_code=True).to("cuda").eval()
+
+processor = AutoProcessor.from_pretrained('Ascetu/yungen', trust_remote_code=True)
+
+
+TITLE = "# [Florence-2-DocVQA Demo](https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA)"
+DESCRIPTION = "The demo for Florence-2 fine-tuned on DocVQA dataset. You can find the notebook [here](https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing). Read more about Florence-2 fine-tuning [here](finetune-florence2)."
+
+
+colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
+            'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
+
+@spaces.GPU
+def run_example(task_prompt, image, text_input=None):
+    if text_input is None:
+        prompt = task_prompt
+    else:
+        prompt = task_prompt + text_input
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(
+        generated_text,
+        task=task_prompt,
+        image_size=(image.width, image.height)
+    )
+    return parsed_answer
+
+def process_image(image, text_input=None):
+    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
+    task_prompt = '<DocVQA>'
+    results = run_example(task_prompt, image, text_input)[task_prompt].replace("<pad>", "")
+    return results
+
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="Florence-2 Image Captioning"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                text_input = gr.Textbox(label="Text Input (optional)")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+
+        gr.Examples(
+            examples=[
+                ["idefics2_architecture.png", 'How many tokens per image does it use?'],
+                ["idefics2_architecture.png", "What type of encoder does the model use?"],
+                ["idefics2_architecture.png", 'Up to which size can the images be?'],
+                ["image.jpg", "What's the share of Industry Switchers Gained?"]
+            ],
+            inputs=[input_img, text_input],
+            outputs=[output_text],
+            fn=process_image,
+            cache_examples=True,
+            label='Try the examples below'
+        )
+
+        submit_btn.click(process_image, [input_img, text_input], [output_text])
+
+demo.launch(debug=True)