Update app.py

app.py

@@ -1,25 +1,21 @@
-#Initial installations
-pip uninstall -y tensorflow
-pip install tensorflow==2.14
+# Initial installations handled separately (not in app.py)
 
-pip install --upgrade pip
-pip install --upgrade transformers scipy
-
-pip install transformers
-pip install pymupdf
-
-## Summarization
+# Required imports
 import gradio as gr
 import fitz  # PyMuPDF
 from transformers import BartTokenizer, BartForConditionalGeneration, pipeline
 import scipy.io.wavfile
 import numpy as np
+from IPython.display import Audio
 
+# Initialize tokenizers and models
 tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
 model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
+synthesiser = pipeline("text-to-speech", "suno/bark")
 
-def extract_abstract(pdf_path):
-    doc = fitz.open(pdf_path)
+# Function to extract abstract from PDF
+def extract_abstract(pdf_content):
+    doc = fitz.open("pdf", pdf_content)
     first_page = doc[0].get_text()
     start_idx = first_page.lower().find("abstract")
     end_idx = first_page.lower().find("introduction")
@@ -28,101 +24,34 @@ def extract_abstract(pdf_path):
     else:
         return "Abstract not found or '1 Introduction' not found in the first page."
 
-# Specify the path to your PDF file
-pdf_path = "/content/article11.pdf"  # Update the path
-
-# Extract the abstract
-abstract_text = extract_abstract(pdf_path)
-
-# Print the extracted abstract
-print("Extracted Abstract:")
-print(abstract_text)
-
-
-from IPython.core.display import display, HTML
-
-# Function to display summary and reduction percentage aesthetically
-def display_results(final_summary, original_text):
-    reduction_percentage = 100 * (1 - len(final_summary) / len(original_text))
-    html_content = f"""
-    <div style='padding: 20px; background-color: #f3f3f3; border-radius: 10px;'>
-        <h2 style='color: #2c3e50; text-align: center;'>Summary</h2>
-        <p style='color: #34495e; font-size: 16px; text-align: justify;'>{final_summary}</p>
-        <p style='color: #2c3e50;'><b>Reduction in Text:</b> {reduction_percentage:.2f}%</p>
-    </div>
-    """
-    display(HTML(html_content))
-
-# Summary generation and post-processing
-inputs = tokenizer([abstract_text], max_length=1024, return_tensors='pt', truncation=True)
-max_length_for_summary = 40
-length_penalty_value = 2.0
-
-summary_ids = model.generate(inputs['input_ids'],
-                             num_beams=4,
-                             max_length=max_length_for_summary,
-                             min_length=10,
-                             length_penalty=length_penalty_value,
-                             early_stopping=True,
-                             no_repeat_ngram_size=2)
-
-summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-summary = ' '.join(summary.split())  # Remove extra spaces
-
-# Handle truncated words and adjust periods
-words = summary.split()
-cleaned_summary = []
-for i, word in enumerate(words):
-    if '-' in word and i < len(words) - 1:
-        word = word.replace('-', '') + words[i + 1]
-        words[i + 1] = ""
-
-    if '.' in word and i != len(words) - 1:
-        word = word.replace('.', '')
-        cleaned_summary.append(word + ' and')
-    else:
-        cleaned_summary.append(word)
-
-# Capitalize first word and adjust following words
-final_summary = ' '.join(cleaned_summary)
-final_summary = final_summary[0].upper() + final_summary[1:]
-final_summary = ' '.join(w[0].lower() + w[1:] if w.lower() != 'and' else w for w in final_summary.split())
-
-# Displaying the results
-display_results(final_summary, abstract_text)
-
-
-##Text-to-Speech
-
-# Initialize the Bark TTS pipeline
-synthesiser = pipeline("text-to-speech", "suno/bark")
-
-# Initialize the Bark TTS pipeline
-synthesiser = pipeline("text-to-speech", "suno/bark")
-
-# Convert the summarized text to speech
-speech = synthesiser(final_summary, forward_params={"do_sample": True})
+# Function to process text (summarize and convert to speech)
+def process_text(pdf_content):
+    abstract_text = extract_abstract(pdf_content)
+    
+    # Generate summary
+    inputs = tokenizer([abstract_text], max_length=1024, return_tensors='pt', truncation=True)
+    summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=40, min_length=10, length_penalty=2.0, early_stopping=True, no_repeat_ngram_size=2)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
-# Normalize the audio data
-audio_data = speech["audio"].squeeze()
-normalized_audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
+    # Convert summary to speech
+    speech = synthesiser(summary, forward_params={"do_sample": True})
+    audio_data = speech["audio"].squeeze()
+    normalized_audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
 
-# Save the normalized audio data as a WAV file
-output_file = "/content/bark_output.wav"
-scipy.io.wavfile.write(output_file, rate=speech["sampling_rate"], data=normalized_audio_data)
-print(f"Audio file saved as {output_file}")
+    # Save audio to temporary file
+    output_file = "temp_output.wav"
+    scipy.io.wavfile.write(output_file, rate=speech["sampling_rate"], data=normalized_audio_data)
 
-# Display an audio player widget to play the generated speech
-Audio(output_file)
+    return summary, output_file
 
 # Gradio Interface
 iface = gr.Interface(
     fn=process_text,
-    inputs="text",
+    inputs=gr.inputs.File(label="Upload PDF"),
     outputs=["text", "audio"],
     title="Summarization and Text-to-Speech",
-    description="Enter text to summarize and convert to speech."
+    description="Upload a PDF to extract, summarize its abstract, and convert to speech."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()