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import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
from datetime import datetime
import numpy as np
import os
DESCRIPTION = """
# VisQA Demo
"""
model_id = "Qwen/Qwen2-VL-7B-Instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
)
adapter_path = "sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA"
model.load_adapter(adapter_path)
processor = Qwen2VLProcessor.from_pretrained(model_id)
def array_to_image_path(image_array):
if image_array is None:
raise ValueError("No image provided. Please upload an image before submitting.")
# Convert numpy array to PIL Image
img = Image.fromarray(np.uint8(image_array))
# Generate a unique filename using timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"image_{timestamp}.png"
# Save the image
img.save(filename)
# Get the full path of the saved image
full_path = os.path.abspath(filename)
return full_path
@spaces.GPU
def run_example(image, text_input=None):
image_path = array_to_image_path(image)
image = Image.fromarray(image).convert("RGB")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{
"type": "text",
"text": text_input
},
],
}
]
# 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=1024)
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]
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="QA "):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
text_input = gr.Textbox(label="Question")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
submit_btn.click(run_example, [input_img, text_input], [output_text])
demo.queue(api_open=False)
demo.launch(debug=True) |