Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForVision2Seq
+import re
+import time
+from PIL import Image
+import torch
+import spaces
+import subprocess
+#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+
+processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
+model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM-Instruct", 
+        torch_dtype=torch.bfloat16,
+        #_attn_implementation="flash_attention_2"
+        ).to("cuda")
+
+@spaces.GPU
+def model_inference(
+    images, text, assistant_prefix, decoding_strategy, temperature, max_new_tokens,
+    repetition_penalty, top_p
+):
+    if text == "" and not images:
+        gr.Error("Please input a query and optionally image(s).")
+
+    if text == "" and images:
+        gr.Error("Please input a text query along the image(s).")
+
+    if isinstance(images, Image.Image):
+        images = [images]
+
+
+    resulting_messages = [
+                {
+                    "role": "user",
+                    "content": [{"type": "image"}] + [
+                        {"type": "text", "text": text}
+                    ]
+                }
+            ]
+
+    if assistant_prefix:
+      text = f"{assistant_prefix} {text}"
+
+
+    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=[images], return_tensors="pt")
+    inputs = {k: v.to("cuda") for k, v in inputs.items()}
+
+    generation_args = {
+        "max_new_tokens": max_new_tokens,
+        "repetition_penalty": repetition_penalty,
+
+    }
+
+    assert decoding_strategy in [
+        "Greedy",
+        "Top P Sampling",
+    ]
+    if decoding_strategy == "Greedy":
+        generation_args["do_sample"] = False
+    elif decoding_strategy == "Top P Sampling":
+        generation_args["temperature"] = temperature
+        generation_args["do_sample"] = True
+        generation_args["top_p"] = top_p
+
+    generation_args.update(inputs)
+
+    # Generate
+    generated_ids = model.generate(**generation_args)
+
+    generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True)
+    return generated_texts[0]
+
+
+with gr.Blocks(fill_height=False) as demo:
+    gr.Markdown("## SmolVLM: Small yet Mighty 💫")
+    gr.Markdown("Play with [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) in this demo. To get started, upload an image and text or try one of the examples.")
+    with gr.Column():
+        with gr.Row():
+            image_input = gr.Image(label="Upload your Image", type="pil")
+
+            with gr.Column():
+                query_input = gr.Textbox(label="Prompt")
+                assistant_prefix = gr.Textbox(label="Assistant Prefix", placeholder="Let's think step by step.")
+
+                submit_btn = gr.Button("Submit")
+        output = gr.Textbox(label="Output")
+
+        
+        with gr.Accordion(label="Advanced Generation Parameters", open=False):
+            examples=[
+                    ["example_images/rococo.jpg", "What art era is this?", "", "Top P Sampling", 0.4, 512, 1.2, 0.8],
+                    ["example_images/examples_wat_arun.jpg", "I'm planning a visit to this temple, give me travel tips.",  "", "Greedy", 0.4, 512, 1.2, 0.8],
+                    ["example_images/examples_invoice.png", "What is the due date and the invoice date?",  "", "Top P Sampling", 0.4, 512, 1.2, 0.8],
+                    ["example_images/s2w_example.png", "What is this UI about?",  "", "Top P Sampling", 0.4, 512, 1.2, 0.8],
+                    ["example_images/examples_weather_events.png", "Where do the severe droughts happen according to this diagram?",  "", "Top P Sampling", 0.4, 512, 1.2, 0.8],
+            ]
+            # Hyper-parameters for generation
+            max_new_tokens = gr.Slider(
+                minimum=8,
+                maximum=1024,
+                value=512,
+                step=1,
+                interactive=True,
+                label="Maximum number of new tokens to generate",
+            )
+            repetition_penalty = gr.Slider(
+                minimum=0.01,
+                maximum=5.0,
+                value=1.2,
+                step=0.01,
+                interactive=True,
+                label="Repetition penalty",
+                info="1.0 is equivalent to no penalty",
+            )
+            temperature = gr.Slider(
+                minimum=0.0,
+                maximum=5.0,
+                value=0.4,
+                step=0.1,
+                interactive=True,
+                label="Sampling temperature",
+                info="Higher values will produce more diverse outputs.",
+            )
+            top_p = gr.Slider(
+                minimum=0.01,
+                maximum=0.99,
+                value=0.8,
+                step=0.01,
+                interactive=True,
+                label="Top P",
+                info="Higher values is equivalent to sampling more low-probability tokens.",
+            )
+            decoding_strategy = gr.Radio(
+                [
+                    "Top P Sampling",
+                    "Greedy",
+                    
+                ],
+                value="Top P Sampling",
+                label="Decoding strategy",
+                interactive=True,
+                info="Higher values is equivalent to sampling more low-probability tokens.",
+            )
+            decoding_strategy.change(
+                fn=lambda selection: gr.Slider(
+                    visible=(
+                        selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
+                    )
+                ),
+                inputs=decoding_strategy,
+                outputs=temperature,
+            )
+
+            decoding_strategy.change(
+                fn=lambda selection: gr.Slider(
+                    visible=(
+                        selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
+                    )
+                ),
+                inputs=decoding_strategy,
+                outputs=repetition_penalty,
+            )
+            decoding_strategy.change(
+                fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
+                inputs=decoding_strategy,
+                outputs=top_p,
+            )
+        gr.Examples(
+                        examples = examples,
+                        inputs=[image_input, query_input, assistant_prefix, decoding_strategy, temperature,
+                                                              max_new_tokens, repetition_penalty, top_p],
+                        outputs=output,
+                        fn=model_inference
+                    )   
+        
+
+        submit_btn.click(model_inference, inputs = [image_input, query_input, assistant_prefix, decoding_strategy, temperature,
+                                                      max_new_tokens, repetition_penalty, top_p], outputs=output)
+
+
+demo.launch(debug=True)