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import streamlit as st
from transformers import AutoModel
from PIL import Image
import torch
import numpy as np
import urllib.request
# Load model without caching due to serialization issue with PretrainedConfig
def load_model():
model = AutoModel.from_pretrained("ragavsachdeva/magi", trust_remote_code=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
return model
@st.cache_data
def read_image_as_np_array(image_path):
if "http" in image_path:
image = Image.open(urllib.request.urlopen(image_path)).convert("L").convert("RGB")
else:
image = Image.open(image_path).convert("L").convert("RGB")
image = np.array(image)
return image
model = load_model()
@st.cache_data
def predict_detections_and_associations(
image_path,
character_detection_threshold,
panel_detection_threshold,
text_detection_threshold,
character_character_matching_threshold,
text_character_matching_threshold,
):
image = read_image_as_np_array(image_path)
with torch.no_grad():
result = model.predict_detections_and_associations(
[image],
character_detection_threshold=character_detection_threshold,
panel_detection_threshold=panel_detection_threshold,
text_detection_threshold=text_detection_threshold,
character_character_matching_threshold=character_character_matching_threshold,
text_character_matching_threshold=text_character_matching_threshold,
)[0]
return result
@st.cache_data
def predict_ocr(
image_path,
character_detection_threshold,
panel_detection_threshold,
text_detection_threshold,
character_character_matching_threshold,
text_character_matching_threshold,
):
if not generate_transcript:
return
image = read_image_as_np_array(image_path)
result = predict_detections_and_associations(
image_path,
character_detection_threshold,
panel_detection_threshold,
text_detection_threshold,
character_character_matching_threshold,
text_character_matching_threshold,
)
text_bboxes_for_all_images = [result["texts"]]
with torch.no_grad():
ocr_results = model.predict_ocr([image], text_bboxes_for_all_images)
return ocr_results
def clear_memory():
st.session_state.memory = {"characters": {}, "transcript": ""}
st.write("Memory cleared.")
model = load_model()
# Display header and UI components
st.markdown(""" <style> ... styles here ... </style> """, unsafe_allow_html=True)
path_to_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
# Memory control button
st.button("Clear Memory", on_click=clear_memory)
st.sidebar.markdown("**Mode**")
generate_detections_and_associations = st.sidebar.toggle("Generate detections and associations", True)
generate_transcript = st.sidebar.toggle("Generate transcript (slower)", False)
st.sidebar.markdown("**Hyperparameters**")
input_character_detection_threshold = st.sidebar.slider('Character detection threshold', 0.0, 1.0, 0.30, step=0.01)
input_panel_detection_threshold = st.sidebar.slider('Panel detection threshold', 0.0, 1.0, 0.2, step=0.01)
input_text_detection_threshold = st.sidebar.slider('Text detection threshold', 0.0, 1.0, 0.25, step=0.01)
input_character_character_matching_threshold = st.sidebar.slider('Character-character matching threshold', 0.0, 1.0, 0.7, step=0.01)
input_text_character_matching_threshold = st.sidebar.slider('Text-character matching threshold', 0.0, 1.0, 0.4, step=0.01)
if path_to_image is not None:
image = read_image_as_np_array(path_to_image)
st.markdown("**Prediction**")
if generate_detections_and_associations or generate_transcript:
result = predict_detections_and_associations(
path_to_image,
input_character_detection_threshold,
input_panel_detection_threshold,
input_text_detection_threshold,
input_character_character_matching_threshold,
input_text_character_matching_threshold,
)
if generate_transcript:
ocr_results = predict_ocr(
path_to_image,
input_character_detection_threshold,
input_panel_detection_threshold,
input_text_detection_threshold,
input_character_character_matching_threshold,
input_text_character_matching_threshold,
)
# Append new characters and transcript to memory
if generate_detections_and_associations:
output = model.visualise_single_image_prediction(image, result)
st.image(output)
# Update character memory based on detected characters
detected_characters = result.get("characters", {})
st.session_state.memory["characters"].update(detected_characters)
# Append the current transcript to the ongoing transcript in memory
transcript = model.generate_transcript_for_single_image(result, ocr_results[0])
st.session_state.memory["transcript"] += transcript + "\n"
# Display the cumulative transcript from memory
st.text(st.session_state.memory["transcript"])
elif generate_detections_and_associations:
output = model.visualise_single_image_prediction(image, result)
st.image(output)
elif generate_transcript:
# Display the cumulative transcript
st.text(st.session_state.memory["transcript"])
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