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Update src/streamlit_app.py

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  1. src/streamlit_app.py +51 -38
src/streamlit_app.py CHANGED
@@ -1,40 +1,53 @@
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- import altair as alt
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- import numpy as np
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- import pandas as pd
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  import streamlit as st
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- """
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- # Welcome to Streamlit!
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-
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- Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
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- If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
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- forums](https://discuss.streamlit.io).
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-
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- In the meantime, below is an example of what you can do with just a few lines of code:
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- """
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-
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- num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
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- num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
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-
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- indices = np.linspace(0, 1, num_points)
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- theta = 2 * np.pi * num_turns * indices
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- radius = indices
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-
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- x = radius * np.cos(theta)
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- y = radius * np.sin(theta)
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-
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- df = pd.DataFrame({
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- "x": x,
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- "y": y,
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- "idx": indices,
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- "rand": np.random.randn(num_points),
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- })
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-
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- st.altair_chart(alt.Chart(df, height=700, width=700)
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- .mark_point(filled=True)
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- .encode(
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- x=alt.X("x", axis=None),
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- y=alt.Y("y", axis=None),
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- color=alt.Color("idx", legend=None, scale=alt.Scale()),
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- size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
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- ))
 
 
 
 
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  import streamlit as st
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+ import easyocr
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+ import cv2
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+ import numpy as np
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+ from PIL import Image
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+ import tempfile
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+ import os
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+
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+ st.set_page_config(page_title="🧾 EasyOCR Image Text Extractor", layout="centered")
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+ st.title("🧾 Image OCR with EasyOCR")
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+
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+ # Upload image file
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+ uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
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+
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+ def process_image(image_path, gpu=False):
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+ reader = easyocr.Reader(['en'], gpu=gpu)
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+ result = reader.readtext(image_path)
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+
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+ text_lines = [detection[1] for detection in result]
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+ saved_text = " ".join(text_lines)
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+
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+ img = cv2.imread(image_path)
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+ for detection in result:
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+ top_left = tuple([int(val) for val in detection[0][0]])
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+ bottom_right = tuple([int(val) for val in detection[0][2]])
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+ text = detection[1]
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+ font = cv2.FONT_HERSHEY_SIMPLEX
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+ img = cv2.rectangle(img, top_left, bottom_right, (0, 255, 0), 2)
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+ img = cv2.putText(img, text, top_left, font, 0.6, (255, 255, 255), 1, cv2.LINE_AA)
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+
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+ return img, saved_text
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+
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+ if uploaded_file:
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+ # Save the uploaded file temporarily
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+ with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
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+ temp_file.write(uploaded_file.read())
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+ temp_path = temp_file.name
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+
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+ # Process image
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+ img_result, extracted_text = process_image(temp_path)
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+
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+ # Show image with bounding boxes
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+ st.image(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB), caption="Detected Text", use_column_width=True)
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+
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+ # Show extracted text
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+ st.subheader("📄 Extracted Text")
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+ st.text_area("Detected Text", extracted_text, height=200)
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+
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+ # Option to download text
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+ st.download_button("Download Extracted Text", extracted_text, file_name="extracted_text.txt")
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+ # Clean up temporary file
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+ os.remove(temp_path)