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()