Create app.py

app.py

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+import streamlit as st
+import easyocr
+import cv2
+import numpy as np
+from PIL import Image
+import tempfile
+import os
+
+st.set_page_config(page_title="🧾 EasyOCR Image Text Extractor", layout="centered")
+st.title("🧾 Image OCR with EasyOCR")
+
+# Upload image file
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+def process_image(image_path, gpu=False):
+    reader = easyocr.Reader(['en'], gpu=gpu)
+    result = reader.readtext(image_path)
+
+    text_lines = [detection[1] for detection in result]
+    saved_text = " ".join(text_lines)
+
+    img = cv2.imread(image_path)
+    for detection in result:
+        top_left = tuple([int(val) for val in detection[0][0]])
+        bottom_right = tuple([int(val) for val in detection[0][2]])
+        text = detection[1]
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        img = cv2.rectangle(img, top_left, bottom_right, (0, 255, 0), 2)
+        img = cv2.putText(img, text, top_left, font, 0.6, (255, 255, 255), 1, cv2.LINE_AA)
+
+    return img, saved_text
+
+if uploaded_file:
+    # Save the uploaded file temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_path = temp_file.name
+
+    # Process image
+    img_result, extracted_text = process_image(temp_path)
+
+    # Show image with bounding boxes
+    st.image(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB), caption="Detected Text", use_column_width=True)
+
+    # Show extracted text
+    st.subheader("📄 Extracted Text")
+    st.text_area("Detected Text", extracted_text, height=200)
+
+    # Option to download text
+    st.download_button("Download Extracted Text", extracted_text, file_name="extracted_text.txt")
+
+    # Clean up temporary file
+    os.remove(temp_path)