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import streamlit as st
import torch
from PIL import Image
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
import time
@st.cache_resource
def load_model():
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32).eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
return model, processor
model, processor = load_model()
st.title("Image Query App")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
st.sidebar.title("Suggested Questions")
predefined_questions = [
"What is the main object in this image?",
"Describe the scene in the image.",
"Are there any people in the image?",
"What is the background of the image?"
]
selected_question = st.sidebar.radio("Choose a question", predefined_questions)
question = st.sidebar.text_input("Or ask your own question here:")
submit_button = st.sidebar.button("Submit")
response = ""
if uploaded_file is not None:
image = Image.open(uploaded_file)
original_size = image.size
st.write(f"Original image dimensions: {original_size}")
max_size = (700, 700)
if image.size[0] > 1000 or image.size[1] > 1000:
image.thumbnail(max_size)
resized_size = image.size
st.write(f"Image resized to: {resized_size}")
else:
st.write("Image size is within acceptable limits.")
if not question:
question = selected_question
if submit_button:
st.sidebar.markdown("<h3 style='color:blue;'>Fetching the answer might take 2-3 minutes depending on the question, hold tight while we process your request!</h3>", unsafe_allow_html=True)
start_time = time.time() # Start the timer
if question:
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": question},
],
}
]
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",
)
with st.spinner('Fetching the answer...'):
with torch.no_grad():
new_generated_ids = model.generate(**inputs, max_new_tokens=180)
new_generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, new_generated_ids)
]
response = processor.batch_decode(
new_generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
else:
st.warning("Please enter a question.")
elapsed_time = time.time() - start_time # Calculate elapsed time
if response:
st.markdown(f"<h4 style='color:green;'>Response:</h4><p style='font-size:18px;'>{response}</p>", unsafe_allow_html=True)
st.markdown(f"<p style='color:gray;'>Time taken to fetch the answer: {elapsed_time:.2f} seconds</p>", unsafe_allow_html=True)
if uploaded_file is not None:
st.image(image, caption='Uploaded Image', use_column_width=True)
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