app.py

import streamlit as st
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import torch

# Load the processor and model directly
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")

# Streamlit app
st.title("Image Description Generator")

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Open the image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    st.write("Generating description...")

    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image,
                },
                {"type": "text", "text": "Describe this image."},
            ],
        }
    ]

    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

    # Pass the image to the processor
    inputs = processor(
        text=[text],
        images=[image],
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cpu")

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=128)
    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
    )
    
    st.write("Description:")
    st.write(output_text[0])