Update app.py

app.py

@@ -1,56 +1,64 @@
-#Required imports
+import logging
 import gradio as gr
 import fitz  # PyMuPDF
 from transformers import BartTokenizer, BartForConditionalGeneration, pipeline
 import scipy.io.wavfile
 import numpy as np
 
+# Initialize logging
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+
 # 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")
 
-# Function to extract abstract from PDF
 def extract_abstract(pdf_bytes):
-    # Open PDF with fitz
-    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
-    first_page = doc[0].get_text()
-    start_idx = first_page.lower().find("abstract")
-    end_idx = first_page.lower().find("introduction")
-    if start_idx != -1 and end_idx != -1:
-        return first_page[start_idx:end_idx].strip()
-    else:
-        return "Abstract not found or 'Introduction' not found in the first page."
-
-# Function to process text (summarize and convert to speech)
+    try:
+        doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+        first_page = doc[0].get_text()
+        start_idx = first_page.lower().find("abstract")
+        end_idx = first_page.lower().find("introduction")
+        if start_idx != -1 and end_idx != -1:
+            return first_page[start_idx:end_idx].strip()
+        else:
+            return "Abstract not found or 'Introduction' not found in the first page."
+    except Exception as e:
+        logging.error(f"Error extracting abstract: {e}")
+        return "Error in abstract extraction"
+
 def process_text(uploaded_file):
-    # Attempt to extract byte content from NamedString object
     try:
         pdf_bytes = uploaded_file.file.read()
-    except AttributeError:
-        print("Error reading file content from uploaded_file")
+        logging.info("PDF file read successfully")
+    except AttributeError as e:
+        logging.error(f"Error reading file content: {e}")
         return "File content could not be retrieved", None
 
-    # Extract abstract from PDF
-    abstract_text = extract_abstract(pdf_bytes)
+    try:
+        abstract_text = extract_abstract(pdf_bytes)
+        logging.info(f"Extracted abstract: {abstract_text[:100]}...")  # Log first 100 chars of abstract
+    except Exception as e:
+        logging.error(f"Error in abstract extraction: {e}")
+        return "Error in processing PDF", None
 
-    # 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)
+    try:
+        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)
 
-    # 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)
+        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 audio to temporary file
-    output_file = "temp_output.wav"
-    scipy.io.wavfile.write(output_file, rate=speech["sampling_rate"], data=normalized_audio_data)
+        output_file = "temp_output.wav"
+        scipy.io.wavfile.write(output_file, rate=speech["sampling_rate"], data=normalized_audio_data)
 
-    return summary, output_file
+        return summary, output_file
+    except Exception as e:
+        logging.error(f"Error in summary generation or TTS conversion: {e}")
+        return "Error in summary or speech generation", None
 
-# Gradio Interface
 iface = gr.Interface(
     fn=process_text,
     inputs=gr.components.File(label="Upload PDF"),