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import streamlit as st
from PIL import Image
import pytesseract
import io
import fitz # PyMuPDF
import cv2
import numpy as np
import requests
from transformers import pipeline
from difflib import SequenceMatcher
import folium
from streamlit_folium import st_folium
import wikipedia
# Load summarization and NER pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
ner_pipeline = pipeline("ner", aggregation_strategy="simple")
# Streamlit App
st.set_page_config(page_title="AI Historical Document Decipher", layout="wide")
st.title("📜 AI-powered Historical Document Deciphering App")
st.sidebar.header("Upload Document")
uploaded_file = st.sidebar.file_uploader("Upload Image or PDF", type=["jpg", "jpeg", "png", "pdf"])
# Function to convert PDF to image
def pdf_to_images(pdf_bytes):
doc = fitz.open(stream=pdf_bytes, filetype="pdf")
images = []
for page in doc:
pix = page.get_pixmap()
img = Image.open(io.BytesIO(pix.tobytes()))
images.append(img)
return images
# Function to enhance image
def enhance_image(image):
img = np.array(image.convert('RGB'))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Denoise
denoised = cv2.fastNlMeansDenoising(gray, h=30)
# Sharpening
kernel = np.array([[0, -1, 0],
[-1, 5,-1],
[0, -1, 0]])
sharpened = cv2.filter2D(denoised, -1, kernel)
# Thresholding (binarization)
_, binary = cv2.threshold(sharpened, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Optional: Resize (sometimes helps OCR)
scale_percent = 150 # percent of original size
width = int(binary.shape[1] * scale_percent / 100)
height = int(binary.shape[0] * scale_percent / 100)
resized = cv2.resize(binary, (width, height), interpolation=cv2.INTER_CUBIC)
return resized
# Function to perform OCR
def perform_ocr(image):
custom_oem_psm_config = r'--oem 3 --psm 6 -c preserve_interword_spaces=1'
text = pytesseract.image_to_string(image, config=custom_oem_psm_config)
return text
# Function to extract named entities
def extract_entities(text):
entities = ner_pipeline(text)
extracted = {}
for ent in entities:
label = ent['entity_group']
extracted.setdefault(label, set()).add(ent['word'])
return extracted
def get_historical_context(entities):
context = {}
for label, values in entities.items():
for item in values:
try:
summary = wikipedia.summary(item, sentences=2)
context[item] = summary
except wikipedia.exceptions.DisambiguationError as e:
context[item] = f"Multiple entries found for '{item}': {e.options[:3]}"
except wikipedia.exceptions.PageError:
context[item] = f"No historical info found for '{item}'."
except Exception as e:
context[item] = f"Error retrieving info: {e}"
return context
# Function to correct OCR errors (suggestions)
def suggest_corrections(original_text):
words = original_text.split()
suggestions = {}
for word in words:
if len(word) > 4 and not word.isnumeric():
close_matches = [w for w in ["document", "historical", "archive", "event", "location"] if SequenceMatcher(None, word.lower(), w).ratio() > 0.75]
if close_matches:
suggestions[word] = close_matches[0]
return suggestions
# Function to generate map
def generate_map(entities):
m = folium.Map(location=[20, 0], zoom_start=2)
if "LOC" in entities:
for location in entities["LOC"]:
# Dummy coordinates for demonstration
folium.Marker(
location=[51.5074, -0.1278], # Example: London
popup=f"Location: {location}",
tooltip=location
).add_to(m)
return m
if uploaded_file:
file_type = uploaded_file.type
# Display and process the uploaded document
if file_type == "application/pdf":
images = pdf_to_images(uploaded_file.read())
else:
images = [Image.open(uploaded_file)]
for image in images:
st.image(image, caption="Uploaded Document", use_container_width=True)
# Enhance image
enhanced = enhance_image(image)
st.image(enhanced, caption="Enhanced Image", use_container_width=True, channels="GRAY")
# Perform OCR
ocr_text = perform_ocr(enhanced)
st.subheader("Extracted Text (OCR)")
st.text_area("Text", ocr_text, height=200)
# Suggest corrections
corrections = suggest_corrections(ocr_text)
if corrections:
st.subheader("AI Suggestions for Possible Corrections")
for original, suggestion in corrections.items():
st.markdown(f"**{original}** ➔ *{suggestion}*")
# Summarize text
if len(ocr_text.strip()) > 50:
summary = summarizer(ocr_text, max_length=60, min_length=20, do_sample=False)[0]['summary_text']
st.subheader("Summary")
st.write(summary)
# Extract entities
entities = extract_entities(ocr_text)
st.subheader("Key Information")
for label, items in entities.items():
st.markdown(f"**{label}**: {', '.join(items)}")
# Provide historical context
context = get_historical_context(entities)
if context:
st.subheader("Historical Context & Insights")
for item, info in context.items():
st.markdown(f"- **{item}**: {info}")
# Visualize map
st.subheader("Locations Mentioned")
map_ = generate_map(entities)
st_folium(map_, width=700)
st.markdown("---")
else:
st.info("Upload an image or PDF of a historical document to begin.")
st.sidebar.markdown("---")
st.sidebar.markdown("Developed by **Cherilyn Marie Deocampo**")
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