Add application file

app.py

@@ -0,0 +1,191 @@
+import gradio as gr
+import transformers
+from transformers import pipeline
+import PyPDF2
+import pdfplumber
+from pdfminer.high_level import extract_pages, extract_text
+from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure
+import re
+import torch
+from datasets import load_dataset
+import soundfile as sf
+from IPython.display import Audio
+import numpy as np
+from datasets import load_dataset
+import sentencepiece as spm
+
+
+
+def text_extraction(element):
+    # Extracting the text from the in-line text element
+    line_text = element.get_text()
+
+    # Find the formats of the text
+    # Initialize the list with all the formats that appeared in the line of text
+    line_formats = []
+    for text_line in element:
+        if isinstance(text_line, LTTextContainer):
+            # Iterating through each character in the line of text
+            for character in text_line:
+                if isinstance(character, LTChar):
+                    # Append the font name of the character
+                    line_formats.append(character.fontname)
+                    # Append the font size of the character
+                    line_formats.append(character.size)
+    # Find the unique font sizes and names in the line
+    format_per_line = list(set(line_formats))
+
+    # Return a tuple with the text in each line along with its format
+    return (line_text, format_per_line)
+
+def read_pdf(pdf_pathy):
+  # create a PDF file object
+  pdfFileObj = open(pdf_pathy, 'rb')
+  # create a PDF reader object
+  pdfReaded = PyPDF2.PdfReader(pdfFileObj)
+
+  # Create the dictionary to extract text from each image
+  text_per_pagy = {}
+  # We extract the pages from the PDF
+  for pagenum, page in enumerate(extract_pages(pdf_pathy)):
+      print("Elaborating Page_" +str(pagenum))
+      # Initialize the variables needed for the text extraction from the page
+      pageObj = pdfReaded.pages[pagenum]
+      page_text = []
+      line_format = []
+      page_content = []
+
+      # Open the pdf file
+      pdf = pdfplumber.open(pdf_pathy)
+
+
+      # Find all the elements
+      page_elements = [(element.y1, element) for element in page._objs]
+      # Sort all the elements as they appear in the page
+      page_elements.sort(key=lambda a: a[0], reverse=True)
+
+      # Find the elements that composed a page
+      for i,component in enumerate(page_elements):
+          # Extract the position of the top side of the element in the PDF
+          pos= component[0]
+          # Extract the element of the page layout
+          element = component[1]
+
+          # Check if the element is a text element
+          if isinstance(element, LTTextContainer):
+              # Check if the text appeared in a table
+                  # Use the function to extract the text and format for each text element
+                  (line_text, format_per_line) = text_extraction(element)
+                  # Append the text of each line to the page text
+                  page_text.append(line_text)
+                  # Append the format for each line containing text
+                  line_format.append(format_per_line)
+                  page_content.append(line_text)
+          
+
+      # Create the key of the dictionary
+      dctkey = 'Page_'+str(pagenum)
+      # Add the list of list as the value of the page key
+      text_per_pagy[dctkey]= [page_text, line_format, page_content]
+
+  # Closing the pdf file object
+  pdfFileObj.close()
+
+
+  return text_per_pagy
+
+#performing a cleaning of the contents
+import re
+
+def clean_text(text):
+    # remove extra spaces
+    text = re.sub(r'\s+', ' ', text)
+
+    return text.strip()
+
+# using the function on the text_per_page_1 dictionary
+for key, value in text_per_pagy.items():
+    cleaned_text = clean_text(' '.join(value[0]))  # value[0] contains the text
+    text_per_pagy[key] = cleaned_text
+
+# Now text_per_pagy is clean
+
+def extract_abstract(text_per_pagy):
+    abstract_text = ""
+    
+    #iterate through each page in the extracted text dictionary
+    for page_num, page_text in text_per_pagy.items():
+        if page_text:
+           # Replace hyphens used for line breaks
+            page_text = page_text.replace("- ", "")
+           
+            # Looking for the start of the abstract
+            start_index = page_text.find("Abstract")
+            if start_index != -1:
+                # Adjust the start index to exclude the word "Abstract" itself
+                # The length of "Abstract" is 8 characters; we also add 1 to skip the space after it
+                start_index += len("Abstract") + 1
+
+                # Searching the possible end markers of the abstract
+                end_markers = ["Introduction", "Summary", "Overview", "Background"]
+                end_index = -1
+
+                for marker in end_markers:
+                    temp_index = page_text.find(marker, start_index)
+                    if temp_index != -1:
+                        end_index = temp_index
+                        break
+
+                # If no end marker found, take entire text after "Abstract"
+                if end_index == -1:
+                    end_index = len(page_text)
+
+                # Extract the abstract text
+                abstract = page_text[start_index:end_index].strip()
+
+                # Add the abstract to the complete text
+                abstract_text += " " + abstract
+
+                break
+
+    return abstract_text
+
+
+
+def main_function(pdf_file):
+    # Converti il PDF in testo
+    text_per_pagy = read_pdf(pdf_file.name)
+
+    # Pulisci e estrai l'abstract
+    for key, value in text_per_pagy.items():
+        cleaned_text = clean_text(' '.join(value[0]))
+        text_per_pagy[key] = cleaned_text
+    abstract_text = extract_abstract(text_per_pagy)
+
+    # Riassumi l'abstract
+    summarizer = pipeline("summarization", model="pszemraj/long-t5-tglobal-base-sci-simplify-elife")
+    summary = summarizer(abstract_text, max_length=50, min_length=30, do_sample=False)[0]['summary_text']
+
+    # Genera l'audio dal riassunto
+    synthesiser = pipeline("text-to-speech", model="microsoft/speecht5_tts")
+    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+    speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+    speech = synthesiser(summary, forward_params={"speaker_embeddings": speaker_embedding})
+
+    # Salva l'audio in un file temporaneo
+    audio_file_path = "summary.wav"
+    sf.write(audio_file_path, speech["audio"], samplerate=speech["sampling_rate"])
+
+    # Restituisci testo e audio
+    return summary, audio_file_path
+
+# Crea l'interfaccia Gradio
+iface = gr.Interface(
+    fn=main_function,
+    inputs=gr.inputs.File(type="pdf"),
+    outputs=[gr.outputs.Textbox(label="Summary Text"), gr.outputs.Audio(label="Summary Audio", type="file")]
+)
+
+# Avvia l'app
+if __name__ == "__main__":
+    iface.launch()