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# import streamlit as st
# from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
# from PIL import Image
# import torch
# import cv2
# import tempfile
# def load_model_and_processor():
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
# model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model.to(device)
# return processor, model, device
# def process_image(uploaded_file):
# image = Image.open(uploaded_file)
# image = image.resize((512, 512))
# return image
# def process_video(uploaded_file):
# tfile = tempfile.NamedTemporaryFile(delete=False)
# tfile.write(uploaded_file.read())
# cap = cv2.VideoCapture(tfile.name)
# ret, frame = cap.read()
# cap.release()
# if not ret:
# return None
# image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# image = image.resize((512, 512))
# return image
# def generate_description(processor, model, device, image, user_question):
# messages = [
# {
# "role": "user",
# "content": [
# {
# "type": "image",
# "image": image,
# },
# {"type": "text", "text": user_question},
# ],
# }
# ]
# text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt")
# inputs = inputs.to(device)
# generated_ids = model.generate(**inputs, max_new_tokens=512)
# 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]
# def main():
# st.title("Media Description Generator")
# processor, model, device = load_model_and_processor()
# uploaded_files = st.file_uploader("Choose images or videos...", type=["jpg", "jpeg", "png", "mp4", "avi", "mov"], accept_multiple_files=True)
# if uploaded_files:
# user_question = st.text_input("Ask a question about the images or videos:")
# if user_question:
# for uploaded_file in uploaded_files:
# file_type = uploaded_file.type.split('/')[0]
# if file_type == 'image':
# image = process_image(uploaded_file)
# st.image(image, caption='Uploaded Image.', use_column_width=True)
# st.write("Generating description...")
# elif file_type == 'video':
# image = process_video(uploaded_file)
# if image is None:
# st.error("Failed to read the video file.")
# continue
# st.image(image, caption='First Frame of Uploaded Video.', use_column_width=True)
# st.write("Generating description...")
# else:
# st.error("Unsupported file type.")
# continue
# description = generate_description(processor, model, device, image, user_question)
# st.write("Description:")
# st.write(description)
# if __name__ == "__main__":
# main()
import streamlit as st
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import torch
import cv2
import tempfile
def load_model_and_processor():
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
return processor, model, device
def process_image(uploaded_file):
image = Image.open(uploaded_file)
image = image.resize((512, 512))
return image
def process_video(uploaded_file):
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
cap = cv2.VideoCapture(tfile.name)
ret, frame = cap.read()
cap.release()
if not ret:
return None
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
image = image.resize((512, 512))
return image
def generate_description(processor, model, device, image, user_question):
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image,
},
{"type": "text", "text": user_question},
],
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt")
inputs = inputs.to(device)
generated_ids = model.generate(**inputs, max_new_tokens=512)
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]
def main():
st.title("Media Description Generator")
processor, model, device = load_model_and_processor()
uploaded_files = st.file_uploader("Choose images or videos...", type=["jpg", "jpeg", "png", "mp4", "avi", "mov"], accept_multiple_files=True)
if uploaded_files:
user_question = st.text_input("Ask a question about the images or videos:")
if user_question:
generate_button = st.button("Generate Descriptions")
if generate_button:
for uploaded_file in uploaded_files:
file_type = uploaded_file.type.split('/')[0]
if file_type == 'image':
image = process_image(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("Generating description...")
elif file_type == 'video':
image = process_video(uploaded_file)
if image is None:
st.error("Failed to read the video file.")
continue
st.image(image, caption='First Frame of Uploaded Video.', use_column_width=True)
st.write("Generating description...")
else:
st.error("Unsupported file type.")
continue
description = generate_description(processor, model, device, image, user_question)
st.write("Description:")
st.write(description)
if __name__ == "__main__":
main() |