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!sudo apt-get install -y poppler-utils
import streamlit as st
from PIL import Image
from byaldi import RAGMultiModalModel
import tempfile
import torch
# Function to upload image, run inference, and display output
def upload_image_and_infer():
# Step 1: Allow user to upload an image file
uploaded_file = st.file_uploader("Upload an image file", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
# Step 2: Save uploaded image to temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
temp_file.write(uploaded_file.read())
temp_path = temp_file.name
# Step 3: Display the uploaded image
image = Image.open(temp_path)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Step 4: Load the RAGMultiModalModel and processor
RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8")
# Assuming `results` contains the page number information
text_query = "extract the details?"
RAG.index(
input_path=temp_path, # Using the uploaded image's temporary path
index_name="image_index",
store_collection_with_index=False,
overwrite=True
)
results = RAG.search(text_query, k=1)
# Step 5: Prepare messages for inference
image_index = results[0]["page_num"] - 1 # Get page number from the search result
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image, # Use the uploaded image
},
{"type": "text", "text": text_query},
],
}
]
# Step 6: Prepare input for the model
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages) # Assuming process_vision_info is defined
# Tokenizing and preparing inputs
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Step 7: Inference and generate 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)
]
# Decode the generated output
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
# Step 8: Display the output in Streamlit
st.write("Generated Output:", output_text)
else:
st.write("Please upload an image.")
# Helper function to process images (replace with actual implementation if needed)
def process_vision_info(messages):
image_inputs = [msg['content'][0]['image'] for msg in messages if 'image' in msg['content'][0]]
video_inputs = [] # Assuming no video inputs for now
return image_inputs, video_inputs
# Run the function inside the Streamlit app
upload_image_and_infer()
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