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import gradio as gr
from sentence_transformers import SentenceTransformer
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from PIL import Image
import torch
from torchvision import io
from typing import Dict
from datetime import datetime
import numpy as np
import base64
import os, io
# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"./Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("./Qwen2-VL-7B-Instruct")
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
def generate_embeddings(text):
model = SentenceTransformer('./all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
return embeddings
def describe_image(image_array):
image_path = array_to_image_path(image)
image = Image.open(image_path)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
},
{"type": "text", "text": "Make a very detailed description of the image."},
],
}
]
text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
inputs = processor(
text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cpu")
# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [
output_ids[len(input_ids) :]
for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
# remove image
os.remove(image_path)
# Extract the detailed description from the response
return output_text, generate_embeddings(output_text)
# Create a Gradio interface
iface = gr.Interface(
fn=describe_image,
inputs=gr.Image(),
outputs=[gr.Textbox(label="Description"), gr.JSON(label="Embeddings")],
title="Image Description with Qwen Model",
description="Upload an image to get a detailed description using the Qwen2-VL-7B-Instruct model."
)
# Launch the app
#iface.launch(share=True)
iface.launch() |