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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() |