Update app.py

app.py

@@ -1,58 +1,32 @@
 import streamlit as st
-from paddleocr import PaddleOCR, draw_ocr
 from PIL import Image
+import easyocr
 import numpy as np
-from langdetect import detect
-from transformers import pipeline
-import torch
 
-# Initialize PaddleOCR for multilingual text recognition
-ocr = PaddleOCR(use_angle_cls=True, lang='ar')  # Using 'ar' to support Arabic scripts like Urdu
+# Initialize EasyOCR Reader for specific languages
+reader = easyocr.Reader(['en', 'ur'])  # Include 'ur' for Urdu
 
-# Load summarization model
-summarizer = pipeline("summarization")
+# Title
+st.title("OCR App with EasyOCR")
 
-def recognize_text(image_path):
-    image = Image.open(image_path)
-    img_array = np.array(image)
-    # OCR processing
-    ocr_results = ocr.ocr(img_array, cls=True)
-    # Extracting text from OCR results
-    detected_text = " ".join([line[1][0] for line in ocr_results[0]])
-    # Language detection and summarization
-    language = detect(detected_text)
-    summary = summarizer(detected_text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
-    return detected_text, language, summary
-
-def display_ocr_results(image, ocr_results):
-    boxes = [line[0] for line in ocr_results[0]]
-    texts = [line[1][0] for line in ocr_results[0]]
-    scores = [line[1][1] for line in ocr_results[0]]
-    font_path = "/path/to/font.ttf"  # Replace with a valid path to a font supporting Urdu/Arabic
-    return draw_ocr(np.array(image), boxes, texts, scores, font_path=font_path)
-
-# Streamlit Interface
-st.title("Multilingual OCR and Text Summarization App")
-st.write("Upload an image or capture one to get OCR results and text summary")
+# File uploader
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
-# Image Upload or Capture
-image_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
-if image_file is not None:
-    with open("uploaded_image.png", "wb") as f:
-        f.write(image_file.getbuffer())
-    st.success("Image uploaded successfully!")
-    image = Image.open("uploaded_image.png")
-    st.image(image, caption="Uploaded Image", use_container_width=True)
-
-    # Perform OCR and display results
-    detected_text, language, summary = recognize_text("uploaded_image.png")
-    st.write("### Detected Text")
-    st.write(detected_text)
-    st.write("### Detected Language")
-    st.write(language)
-    st.write("### Text Summary")
-    st.write(summary)
+if uploaded_file is not None:
+    # Open and display the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Convert to numpy array for OCR
+    img_array = np.array(image)
 
-    # Display OCR visualization
-    visualized_image = display_ocr_results(image, ocr.ocr(np.array(image), cls=True))
-    st.image(visualized_image, caption="OCR Results Visualization", use_container_width=True)
+    # Perform OCR
+    with st.spinner("Processing..."):
+        results = reader.readtext(img_array)
+    
+    # Display results
+    detected_text = ""
+    for (bbox, text, prob) in results:
+        detected_text += f"{text} "
+    
+    st.write("Detected Text:", detected_text)