Create app.py

app.py

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+import gradio as gr
+import re
+import json
+from pyidaungsu import pds
+
+# Function to split text into bite-sized paragraphs
+def split_text_into_paragraphs(text):
+    # Define sentence-ending punctuation for Burmese
+    sentence_endings = re.compile(r'။|။\s+')
+
+    # Split text by sentences
+    sentences = sentence_endings.split(text)
+
+    # Join sentences into bite-sized paragraphs
+    paragraphs = []
+    paragraph = ""
+    for sentence in sentences:
+        if len(paragraph) + len(sentence) < 1000:  # Adjust the size limit as needed
+            paragraph += sentence + "။"
+        else:
+            paragraphs.append(paragraph.strip())
+            paragraph = sentence + "။"
+    if paragraph:
+        paragraphs.append(paragraph.strip())
+
+    return paragraphs
+
+# Function to extract keywords using the pyidaungsu library
+def extract_keywords(paragraph):
+    # Tokenize the paragraph into words
+    words = pds.tokenize(paragraph, form="word")
+    # Extract keywords (this can be further improved with more advanced techniques)
+    keywords = [word for word in words if len(word) > 1]  # Simple keyword extraction
+    return keywords
+
+# Main processing function
+def process_text(text):
+    paragraphs = split_text_into_paragraphs(text)
+    result = []
+    for para in paragraphs:
+        keywords = extract_keywords(para)
+        result.append({"paragraph": para, "keywords": keywords})
+    return json.dumps(result, ensure_ascii=False, indent=2)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_text,
+    inputs="text",
+    outputs="text",
+    title="Lazy Reader",
+    description="This app takes large Burmese text without line breaks as input and outputs a JSON of each paragraph and keywords to fetch images related to the paragraph."
+)
+
+# Launch the Gradio app
+iface.launch()