app.py

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from prompt import smoke_detection_prompt
import gradio as gr
import spaces

model_name = "leon-se/ForestFireVLM-7B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_name, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_name)

@spaces.GPU(duration=120)
def generate(image):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image,
                },
                {"type": "text", "text": smoke_detection_prompt},
            ],
        }
    ]

    # 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=300, do_sample=False)
    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[0]

inputs = gr.Image(type="pil", label="Input Image")
outputs = gr.JSON(label="Output")

title = "ForestFireVLM"
description = "This is ForestFireVLM-7B, a finetune of Qwen2.5-VL-7B-Instruct. Our demo shows how Vision-Language Models can give detailled and structured captions for forest fires from UAV perspectives."

demo = gr.Interface(fn=generate, inputs=inputs, outputs=outputs, deep_link=False, title=title, description=description)
demo.launch()