app.py

import gradio as gr
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import time
import torch
import spaces

DESCRIPTION = """
# Qwen2.5-VL-3B/7B-Instruct
"""

css = '''
h1 {
  text-align: center;
  display: block;
}
#duplicate-button {
  margin: auto;
  color: #fff;
  background: #1565c0;
  border-radius: 100vh;
}
'''

# Define an animated progress bar HTML snippet
def progress_bar_html(label: str) -> str:
    return f'''
    <div style="display: flex; align-items: center;">
        <span style="margin-right: 10px; font-size: 14px;">{label}</span>
        <div style="width: 110px; height: 5px; background-color: #FFF0F5; border-radius: 2px; overflow: hidden;">
            <div style="width: 100%; height: 100%; background-color: #FF69B4; animation: loading 1.5s linear infinite;"></div>
        </div>
    </div>
    <style>
    @keyframes loading {{
        0% {{ transform: translateX(-100%); }}
        100% {{ transform: translateX(100%); }}
    }}
    </style>
    '''

# Model IDs for 3B and 7B variants
MODEL_ID_3B = "Qwen/Qwen2.5-VL-3B-Instruct"
MODEL_ID_7B = "Qwen/Qwen2.5-VL-7B-Instruct"

# Load the processor and models for both versions
processor_3b = AutoProcessor.from_pretrained(MODEL_ID_3B, trust_remote_code=True)
model_3b = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_3B,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to("cuda").eval()

processor_7b = AutoProcessor.from_pretrained(MODEL_ID_7B, trust_remote_code=True)
model_7b = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_7B,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to("cuda").eval()

@spaces.GPU
def model_inference(input_dict, history):
    text = input_dict["text"]
    files = input_dict["files"]

    # Determine which model to use based on the prefix tag
    if text.lower().startswith("@3b"):
        yield progress_bar_html("processing with Qwen2.5-VL-3B-Instruct")
        selected_model = model_3b
        selected_processor = processor_3b
        text = text[len("@3b"):].strip()
    elif text.lower().startswith("@7b"):
        yield progress_bar_html("processing with Qwen2.5-VL-7B-Instruct")
        selected_model = model_7b
        selected_processor = processor_7b
        text = text[len("@7b"):].strip()
    else:
        yield "Error: Please prefix your query with @3b or @7b to select the model."
        return

    # Load images if provided
    if files:
        if isinstance(files, list):
            if len(files) > 1:
                images = [load_image(image) for image in files]
            elif len(files) == 1:
                images = [load_image(files[0])]
            else:
                images = []
        else:
            images = [load_image(files)]
    else:
        images = []

    # Validate input: text query is required
    if text == "":
        yield "Error: Please input a text query along with the image(s) if any."
        return

    # Prepare messages for the model
    messages = [{
        "role": "user",
        "content": [
            *[{"type": "image", "image": image} for image in images],
            {"type": "text", "text": text},
        ]
    }]

    # Apply the chat template and process the inputs
    prompt = selected_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = selected_processor(
        text=[prompt],
        images=images if images else None,
        return_tensors="pt",
        padding=True,
    ).to("cuda")

    # Set up a streamer for real-time text generation
    streamer = TextIteratorStreamer(selected_processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)

    # Start generation in a separate thread
    thread = Thread(target=selected_model.generate, kwargs=generation_kwargs)
    thread.start()

    # Yield an animated progress message
    yield progress_bar_html("Thinking...")

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        yield buffer

# Example inputs with model prefixes
examples = [
    [{"text": "@3b Describe the document?", "files": ["example_images/document.jpg"]}],
    [{"text": "@7b What does this say?", "files": ["example_images/math.jpg"]}],
    [{"text": "@3b What is this UI about?", "files": ["example_images/s2w_example.png"]}],
    [{"text": "@7b Where do the severe droughts happen according to this diagram?", "files": ["example_images/examples_weather_events.png"]}],
]

demo = gr.ChatInterface(
    fn=model_inference,
    description=DESCRIPTION,
    css=css,
    examples=examples,
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple", placeholder="Use Tags @3b / @7b to trigger the models"),
    stop_btn="Stop Generation",
    multimodal=True,
    cache_examples=False,
)

demo.launch(debug=True)