Update app.py

app.py

@@ -8,6 +8,10 @@ def process_and_explain(pdf_file, concept_to_explain):
 import gradio as gr
 from transformers import pipeline
 import PyPDF2
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Load the explainer model
 explainer = pipeline("text2text-generation", model="google/flan-t5-base")
@@ -17,23 +21,36 @@ def extract_text_from_pdf(pdf_file):
     try:
         with open(pdf_file.name, 'rb') as pdfFileObj:
             pdfReader = PyPDF2.PdfReader(pdfFileObj)
-            for pageNum in range(len(pdfReader.pages)):
+            num_pages = len(pdfReader.pages)
+            logger.info(f"Number of pages in PDF: {num_pages}")
+            for pageNum in range(num_pages):
                 pageObj = pdfReader.pages[pageNum]
-                text += pageObj.extract_text()
+                page_text = pageObj.extract_text()
+                text += page_text
+            logger.info(f"Extracted {len(text)} characters from PDF.")
     except Exception as e:
+        logger.error(f"Error reading PDF: {e}")
         return f"Error reading PDF: {e}"
     return text
 
 def process_and_explain(pdf_file, concept_to_explain):
+    logger.info("Processing request...")
     if pdf_file is not None:
         extracted_text = extract_text_from_pdf(pdf_file)
+        logger.info(f"Extracted text (first 500 chars):\n{extracted_text[:500]}")
         if "Error reading PDF" in extracted_text or not extracted_text.strip():
             return "Could not extract text from the PDF. Please try another file."
 
-        prompt = f"Explain the concept of '{concept_to_explain}' based on this text: '{extracted_text[:1500]}...' in simple terms." # Limiting text for the model
-        explanation_result = explainer(prompt, max_length=300, do_sample=False)
-        explanation = explanation_result[0]["generated_text"]
-        return f"**Extracted Text Snippet:**\n{extracted_text[:500]}...\n\n**Explanation of '{concept_to_explain}':**\n{explanation}"
+        prompt = f"Explain the concept of '{concept_to_explain}' based on this text: '{extracted_text[:1500]}...' in simple terms."
+        logger.info(f"Prompting explainer model with: {prompt[:200]}...")
+        try:
+            explanation_result = explainer(prompt, max_length=300, do_sample=False)
+            explanation = explanation_result[0]["generated_text"]
+            logger.info(f"Explanation generated: {explanation}")
+            return f"**Extracted Text Snippet:**\n{extracted_text[:500]}...\n\n**Explanation of '{concept_to_explain}':**\n{explanation}"
+        except Exception as e:
+            logger.error(f"Error during explanation generation: {e}")
+            return f"An error occurred while generating the explanation: {e}"
     else:
         return "Please upload a PDF file."