Delete app.py

app.py

@@ -1,69 +0,0 @@
-#imports
-!pip install PyPDF2
-import PyPDF2
-import re
-!pip install transformers
-import transformers
-from transformers import pipeline
-!pip install git+https://github.com/suno-ai/bark.git
-from bark import SAMPLE_RATE, generate_audio, preload_models
-from scipy.io.wavfile import write as write_wav
-from IPython.display import Audio
-
-def abstract_to_audio(insert_pdf):
-    # Extracting the abstract text from the article pdf
-    def extract_abstract(pdf_file):
-        # Open the PDF file in read-binary mode
-        with open(pdf_file, 'rb') as file:
-            # Create a PDF reader object
-            pdf_reader = PyPDF2.PdfReader(file)
-    
-            # Initialize an empty string to store abstract content
-            abstract_text = ''
-    
-            # Loop through each page in the PDF
-            for page_num in range(len(pdf_reader.pages)):
-                # Get the text from the current page
-                page = pdf_reader.pages[page_num]
-                text = page.extract_text()
-    
-                # Use regular expression to find the "Abstract" section
-                abstract_match = re.search(r'\bAbstract\b', text, re.IGNORECASE)
-                if abstract_match:
-                    # Get the text after the "Abstract" heading until the next section, indicated by "Introduction" heading
-                    start_index = abstract_match.end()
-                    next_section_match = re.search(r'\bIntroduction\b', text[start_index:])
-                    if next_section_match:
-                        end_index = start_index + next_section_match.start()
-                        abstract_text = text[start_index:end_index]
-                    else:
-                        # If no next section found, extract text till the end
-                        abstract_text = text[start_index:]
-                    break  # Exit loop once abstract is found
-    
-            return abstract_text.strip()
-    
-    
-    abstract = extract_abstract(insert_pdf)
-    
-    # Creating a summarization pipeline
-    model = "lidiya/bart-large-xsum-samsum"
-    pipeline1 = pipeline(task = "summarization", model = model)
-    
-    # Summarizing the extracted abstract
-    summarized = pipeline1(abstract)
-    print(summarized[0]['summary_text'])
-    tss_prompt = summarized[0]['summary_text']
-    
-    # Generate audio file that speaks the generated sentence using Bark
-    # download and load all models
-    preload_models()
-    
-    # generate audio from text
-    text_prompt = tss_prompt
-    audio_array = generate_audio(text_prompt)
-    
-    # play text in notebook
-    return Audio(audio_array, rate=SAMPLE_RATE)
-
-my_app = gr.Interface(fn=abstract_to_audio, inputs='file', outputs='audio')