Create app.py

app.py

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+import warnings
+import pdfplumber
+import torch
+from transformers import pipeline, AutoProcessor, AutoModel
+import numpy as np
+import gradio as gr
+from io import BytesIO
+from scipy.io.wavfile import write as write_wav
+warnings.filterwarnings("ignore")
+
+
+# Here is the code
+def extract_abstract(uploaded_file):
+    pdf_bytes = BytesIO(uploaded_file)
+    with pdfplumber.open(pdf_bytes) as pdf:
+        abstract = ""
+        # Iterate through each page
+        for page in pdf.pages:
+            text = page.extract_text(x_tolerance = 1, y_tolerance = 1) # these parameters are set 1 in order to get spaces between words and lines
+            # Search for the "Abstract" keyword
+            if "abstract" in text.lower():
+                # Found the "Abstract" keyword
+                start_index = text.lower().find("abstract") # find the "abstract" title as starter index
+                end_index = text.lower().find("introduction") # find the "introduction" title as end index
+                abstract = text[start_index:end_index]
+                break
+    print(abstract)
+    return abstract
+
+def process_summary(summary):
+    # Split the summary by the first period
+    summary = summary[0]["summary_text"]
+    sentences = summary.split('.', 1)
+    if len(sentences) > 0:
+        # Retrieve the first part before the period
+        processed_summary = sentences[0].strip() + "."
+        # Replace "-" with an empty string
+        processed_summary = processed_summary.replace("-", "")
+        return processed_summary
+
+# Function for summarization and audio conversion
+def summarize_and_convert_to_audio(pdf_file):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(device)
+    
+    # Move models and related tensors to CUDA device if available
+    processor = AutoProcessor.from_pretrained("suno/bark-small")
+    model = AutoModel.from_pretrained("suno/bark-small").to(device)
+    
+    # Extract abstract
+    abstract_text = extract_abstract(pdf_file)
+
+    if not abstract_text:
+        return "No 'abstract' section found in the uploaded PDF. Please upload a different PDF."
+    
+    # Summarize the abstract
+    summarization_pipeline = pipeline(task='summarization', model='knkarthick/MEETING_SUMMARY', min_length=15, max_length=30)
+    summarized_text = summarization_pipeline(abstract_text)
+    one_sentence_summary = process_summary(summarized_text)
+
+    print(one_sentence_summary)
+    
+    # Text-to-audio conversion
+    inputs = processor(
+        text=[one_sentence_summary],
+        return_tensors="pt",
+    )
+    inputs = inputs.to(device)
+    
+    speech_values = model.generate(**inputs, do_sample=True)
+    sampling_rate = model.generation_config.sample_rate
+    
+    # Convert speech values to audio data
+    audio_data = speech_values.cpu().numpy().squeeze()
+
+    # Convert audio data to bytes
+    with BytesIO() as buffer:
+        write_wav(buffer, sampling_rate, audio_data.astype(np.float32))
+        audio_bytes = buffer.getvalue()
+    
+    return audio_bytes, one_sentence_summary
+
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=summarize_and_convert_to_audio,
+    inputs=gr.UploadButton(label="Upload PDF", type="binary", file_types=["pdf"]),  # Set to accept only PDF files
+    outputs=[gr.Audio(label="Audio"), gr.Textbox(label="Message")],
+    title="PDF Abstract Summarizer",
+    description="""
+    This application is supposed to summarize the 'abstract' section of a PDF file and convert the summarization into a speech.
+    Please make sure you upload a PDF file with the 'abstract' section for application to work.
+    Note: If you get an error while processing the file please refresh your browser and try again.
+    """
+)
+
+iface.launch()