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"""Alias entrypoint for Streamlit on Hugging Face Spaces.
This is a copy of app.py to match Spaces' default file naming.
"""
import streamlit as st
from PIL import Image
import fitz # PyMuPDF
import numpy as np
import tempfile
import os
import time
import io
import json
import torch
import cv2
# Import OCR engines
import ocr_engines
# Try importing LLM processor if LLM features are to be used
llm_available = False
try:
import llm_processor
llm_available = True
except ImportError:
pass # LLM features will be disabled
# Create results folder if it doesn't exist
if not os.path.exists("results"):
os.makedirs("results")
# Streamlit application
st.title("OCRInsight")
# Sidebar
st.sidebar.header("Settings")
# Function to save text to file
def save_text_to_file(attributes_of_output, all_ocr_text, filename):
with open(filename, "a", encoding="utf-8") as f:
f.write("\n" + "-" * 75 + "\n")
f.write("Attributes of Output:\n")
f.write(attributes_of_output)
f.write("\nOCR Result:\n")
f.write(all_ocr_text)
f.write("\n" + "-" * 75 + "\n")
st.success(f"{filename} saved successfully!")
# Device selection
device = st.sidebar.radio("Select Device", ["CPU", "GPU (CUDA)"])
save_output = st.sidebar.checkbox("Save Outputs")
# Language selection
language = st.sidebar.selectbox(
"Select Language", ["Türkçe", "English", "Français", "Deutsch", "Español"]
)
# Map selected language to language codes
language_codes = {
"Türkçe": "tr",
"English": "en",
"Français": "fr",
"Deutsch": "de",
"Español": "es",
}
# OCR model selection
ocr_models = st.sidebar.multiselect(
"Select OCR Models",
["EasyOCR", "DocTR", "Tesseract", "PaddleOCR"],
["EasyOCR"], # default selection
)
# LLM model selection
llm_model = st.sidebar.selectbox(
"Select LLM Model", ["Only OCR Mode", "llama3.1", "llama3", "gemma2"]
)
# Conditional UI elements based on LLM model selection
if llm_model != "Only OCR Mode" and llm_available:
user_command = st.sidebar.text_input("Enter command:", "")
task_type = st.sidebar.radio("Select task type:", ["Summarize", "Generate"])
elif llm_model != "Only OCR Mode" and not llm_available:
st.sidebar.warning(
"LLM features are not available. Please install 'ollama' to enable LLM processing."
)
llm_model = "Only OCR Mode"
# Check GPU availability
if device == "GPU (CUDA)" and not torch.cuda.is_available():
st.sidebar.warning("GPU (CUDA) not available. Switching to CPU.")
device = "CPU"
# Initialize OCR models
ocr_readers = ocr_engines.initialize_ocr_models(
ocr_models, language_codes[language], device
)
# File upload
uploaded_file = st.file_uploader(
"Upload File (PDF, Image)", type=["pdf", "png", "jpg", "jpeg"]
)
# Create results folder if it doesn't exist
if not os.path.exists("results"):
os.makedirs("results")
if uploaded_file is not None:
start_time = time.time()
if uploaded_file.type == "application/pdf":
pdf_document = fitz.open(stream=uploaded_file.read(), filetype="pdf")
images = []
for page_num in range(len(pdf_document)):
page = pdf_document.load_page(page_num)
pix = page.get_pixmap()
img_data = pix.tobytes("png")
img = Image.open(io.BytesIO(img_data))
images.append(img)
total_pages = len(pdf_document)
pdf_document.close()
else:
images = [Image.open(uploaded_file)]
total_pages = 1
all_ocr_texts = {
model_name: "" for model_name in ocr_models
} # To store OCR text for each model
for page_num, image in enumerate(images, start=1):
st.image(image, caption=f"Page {page_num}/{total_pages}", use_column_width=True)
# Perform OCR with each selected model
for model_name in ocr_models:
text = ocr_engines.perform_ocr(
model_name, ocr_readers, image, language_codes[language]
)
all_ocr_texts[
model_name
] += f"--- Page {page_num} ({model_name}) ---\n{text}\n\n"
st.subheader(f"OCR Result ({model_name}) - Page {page_num}/{total_pages}:")
st.text(text)
end_time = time.time()
process_time = end_time - start_time
st.info(f"Processing time: {process_time:.2f} seconds")
# Save OCR outputs if selected
if save_output:
attributes_of_output = {
"Model Names": ocr_models,
"Language": language,
"Device": device,
"Process Time": process_time,
}
for model_name, ocr_text in all_ocr_texts.items():
filename = f"results//ocr_output_{model_name}.txt"
save_text_to_file(
json.dumps(attributes_of_output, ensure_ascii=False), ocr_text, filename
)
# LLM processing
if (
llm_model != "Only OCR Mode"
and llm_available
and st.sidebar.button("Start LLM Processing")
):
st.subheader("LLM Processing Result:")
# Combine all OCR texts
combined_ocr_text = "\n".join(all_ocr_texts.values())
# Prepare the prompt based on the task type
if task_type == "Summarize":
prompt = f"Please summarize the following text. Command: {user_command}\n\nText: {combined_ocr_text}"
else: # "Generate"
prompt = f"Please generate new text based on the following text. Command: {user_command}\n\nText: {combined_ocr_text}"
llm_output = llm_processor.process_with_llm(llm_model, prompt)
# Display the result
st.write(f"Processing completed using '{llm_model}' model.")
st.text_area("LLM Output:", value=llm_output, height=300)
# Save LLM output if selected
if save_output:
filename = "llm_output.txt"
save_text_to_file(llm_output, "", filename)
elif llm_model != "Only OCR Mode" and not llm_available:
st.warning(
"LLM features are not available. Please install 'ollama' to enable LLM processing."
)
st.sidebar.info(f"Selected device: {device}")
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