Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import FuyuForCausalLM, AutoTokenizer
+from transformers.models.fuyu.processing_fuyu import FuyuProcessor
+from transformers.models.fuyu.image_processing_fuyu import FuyuImageProcessor
+from PIL import Image
+
+model_id = "adept/fuyu-8b"
+dtype = torch.bfloat16
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = FuyuForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=dtype)
+processor = FuyuProcessor(image_processor=FuyuImageProcessor(), tokenizer=tokenizer)
+
+caption_prompt = "Generate a coco-style caption.\\n"
+
+def resize_to_max(image, max_width=1080, max_height=1080):
+    width, height = image.size
+    if width <= max_width and height <= max_height:
+        return image
+
+    scale = min(max_width/width, max_height/height)
+    width = int(width*scale)
+    height = int(height*scale)
+
+    return image.resize((width, height), Image.LANCZOS)
+
+def predict(image, prompt):
+    # image = image.convert('RGB')
+    image = resize_to_max(image)
+
+    model_inputs = processor(text=prompt, images=[image])
+    model_inputs = {k: v.to(dtype=dtype if torch.is_floating_point(v) else v.dtype, device=device) for k,v in model_inputs.items()}
+
+    generation_output = model.generate(**model_inputs, max_new_tokens=40)
+    prompt_len = model_inputs["input_ids"].shape[-1]
+    return tokenizer.decode(generation_output[0][prompt_len:], skip_special_tokens=True)
+
+def caption(image):
+    return predict(image, caption_prompt)
+
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+
+
+css = """
+  #mkd {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+            <h1 id="title">Fuyu Multimodal Demo</h1>
+            <h3><a href="https://hf.co/adept/fuyu-8b">Fuyu-8B</a> is a multimodal model that supports a variety of tasks combining text and image prompts.</h3>
+            For example, you can use it for captioning by asking it to describe an image. You can also ask it questions about an image, a task known as Visual Question Answering, or VQA. This demo lets you explore captioning and VQA, with more tasks coming soon :)
+            Learn more about the model in <a href="https://www.adept.ai/blog/fuyu-8b">our blog post</a>.
+            <br>
+          	<br>
+            <strong>Note: This is a raw model release. We have not added further instruction-tuning, postprocessing or sampling strategies to control for undesirable outputs. The model may hallucinate, and you should expect to have to fine-tune the model for your use-case!</strong>
+            <h3>Play with Fuyu-8B in this demo! 💬</h3>
+        """
+    )
+    with gr.Tab("Visual Question Answering"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(label="Upload your Image", type="pil")
+                text_input = gr.Textbox(label="Ask a Question")
+            vqa_output = gr.Textbox(label="Output")
+            
+        vqa_btn = gr.Button("Answer Visual Question")
+        
+        gr.Examples(
+            [["assets/vqa_example_1.png", "How is this made?"], ["assets/vqa_example_2.png", "What is this flower and where is it's origin?"]],
+            inputs = [image_input, text_input],
+            outputs = [vqa_output],
+            fn=predict,
+            cache_examples=True,
+            label='Click on any Examples below to get VQA results quickly 👇'
+            )
+
+        
+    with gr.Tab("Image Captioning"):
+        with gr.Row():
+            captioning_input = gr.Image(label="Upload your Image", type="pil")
+            captioning_output = gr.Textbox(label="Output")
+        captioning_btn = gr.Button("Generate Caption")
+
+        gr.Examples(
+            [["assets/captioning_example_1.png"], ["assets/captioning_example_2.png"]],
+            inputs = [captioning_input],
+            outputs = [captioning_output],
+            fn=caption,
+            cache_examples=True,
+            label='Click on any Examples below to get captioning results quickly 👇'
+            )
+        
+    captioning_btn.click(fn=caption, inputs=captioning_input, outputs=captioning_output)
+    vqa_btn.click(fn=predict, inputs=[image_input, text_input], outputs=vqa_output)
+
+    
+demo.launch(server_name="0.0.0.0")