Update app.py

app.py

@@ -4,9 +4,10 @@ from PIL import Image
 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='en')  # For language options, use 'ch' for Chinese, etc.
+ocr = PaddleOCR(use_angle_cls=True, lang='ar')  # Using 'ar' to support Arabic scripts like Urdu
 
 # Load summarization model
 summarizer = pipeline("summarization")
@@ -14,24 +15,21 @@ summarizer = pipeline("summarization")
 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]]
-    return draw_ocr(np.array(image), boxes, texts, scores, font_path='path_to_font.ttf')
+    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")
@@ -39,13 +37,12 @@ st.write("Upload an image or capture one to get OCR results and text summary")
 
 # 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_column_width=True)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
 
     # Perform OCR and display results
     detected_text, language, summary = recognize_text("uploaded_image.png")
@@ -58,5 +55,4 @@ if image_file is not None:
 
     # 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_column_width=True)
-
+    st.image(visualized_image, caption="OCR Results Visualization", use_container_width=True)