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import streamlit as st
from PIL import Image
import pytesseract
import requests
# Set the path to Tesseract-OCR engine on your system
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
# Function to extract text from the image using Tesseract
def extract_text_from_image(image):
text = pytesseract.image_to_string(image, lang='eng+hin')
return text
# Function to highlight the keyword in yellow and bold in the extracted text
def highlight_text(text, keyword):
# Split text into lines to highlight keywords in their respective lines
lines = text.splitlines()
highlighted_lines = []
for line in lines:
highlighted_line = line.replace(keyword, f"<span style='background-color: yellow; font-weight: bold;'>{keyword}</span>")
highlighted_lines.append(highlighted_line)
# Join the lines back into a single string
return "<br>".join(highlighted_lines)
# Function to get the meaning of the searched keyword using the Dictionary API
def get_keyword_meaning(keyword):
# Handler function to retrieve word meaning
def handler(word):
url = f"https://api.dictionaryapi.dev/api/v2/entries/en/{word}"
r = requests.get(url)
return r.json()
# Call the handler function with the keyword
data = handler(keyword)
print(f"Response Data: {data}") # Debugging output
if isinstance(data, list) and len(data) > 0:
try:
meanings = data[0]['meanings'][0]['definitions']
meaning = meanings[0]['definition'] if meanings else "Meaning not found."
except (IndexError, KeyError):
meaning = "Meaning not found."
elif isinstance(data, dict) and 'message' in data:
meaning = data['message'] # For error messages
else:
meaning = "Could not retrieve meaning. Check your internet connection."
return meaning
# Streamlit application
st.title("OCR Web Application")
# Step 1: Upload image file (JPEG, PNG)
uploaded_file = st.file_uploader("Upload an image file (JPEG, PNG)", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Step 2: Open and display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Step 3: Extract text from the image
extracted_text = extract_text_from_image(image)
st.subheader("Extracted Text:")
# Display the extracted text as-is with line breaks
st.text(extracted_text) # Use st.text to preserve the formatting
# Step 4: Search for keywords in the extracted text
search_keyword = st.text_input("Enter keyword to search in the extracted text:")
if search_keyword:
if search_keyword.lower() in extracted_text.lower():
# Highlight matching keywords in the extracted text
highlighted_text = highlight_text(extracted_text, search_keyword)
st.subheader("Search Results:")
# Display the highlighted text as HTML to apply the background color and bold text
st.markdown(highlighted_text, unsafe_allow_html=True)
# Step 5: Get and display the meaning of the searched keyword
meaning = get_keyword_meaning(search_keyword.lower())
st.subheader(f"Meaning of '{search_keyword}':")
st.write(meaning)
else:
# No matches found message
st.subheader("Search Results:")
st.write("No matches found.")
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