[Feature] Summarise extracted text from image

app.py

@@ -1 +1,85 @@
-print('hello world')
+import cv2
+import pytesseract
+from PIL import Image
+import nltk
+from nltk.data import find
+from nltk.tokenize import sent_tokenize
+from nltk.corpus import stopwords
+from nltk.probability import FreqDist
+from nltk.tokenize import word_tokenize
+from heapq import nlargest
+
+def ensure_nltk_data():
+    resources = [
+        ('tokenizers/punkt', 'punkt'),
+        ('corpora/stopwords', 'stopwords'),
+        ('corpora/wordnet', 'wordnet')
+    ]
+
+    for resource_path, resource_name in resources:
+        try:
+            find(resource_path)
+        except LookupError:
+            nltk.download(resource_name)
+
+ensure_nltk_data()
+
+def setup_tesseract(tesseract_cmd_path):
+    pytesseract.pytesseract.tesseract_cmd = tesseract_cmd_path
+
+def convert_image_to_text(image_path):
+    try:
+        image = cv2.imread(image_path)
+        if image is None:
+            raise ValueError(f"Image not found at the path: {image_path}")
+
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        text = pytesseract.image_to_string(rgb_image)
+
+        return text
+    except Exception as e:
+        print(f"Error: {e}")
+        return ""
+
+def summarize_text(text, n_sentences=3):
+    stop_words = set(stopwords.words('english'))
+    words = word_tokenize(text.lower())
+    filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
+
+    word_frequencies = FreqDist(filtered_words)
+    max_frequency = max(word_frequencies.values())
+    for word in word_frequencies.keys():
+        word_frequencies[word] = (word_frequencies[word] / max_frequency)
+
+    sentence_scores = {}
+    sentences = sent_tokenize(text)
+    for sentence in sentences:
+        for word in word_tokenize(sentence.lower()):
+            if word in word_frequencies.keys():
+                if sentence not in sentence_scores.keys():
+                    sentence_scores[sentence] = word_frequencies[word]
+                else:
+                    sentence_scores[sentence] += word_frequencies[word]
+
+    summary_sentences = nlargest(n_sentences, sentence_scores, key=sentence_scores.get)
+    summary = ' '.join(summary_sentences)
+    return summary
+
+def main(image_path, tesseract_cmd_path):
+    setup_tesseract(tesseract_cmd_path)
+    text = convert_image_to_text(image_path)
+    if text:
+        print("Extracted Text:")
+        print(text)
+
+        summary = summarize_text(text)
+        print("\nSummary:")
+        print(summary)
+    else:
+        print("No text extracted.")
+
+if __name__ == "__main__":
+    image_path = '' # TODO: Add file path to image
+    tesseract_cmd_path = r'C:\Program Files\Tesseract-OCR\tesseract.exe' 
+    main(image_path, tesseract_cmd_path)