Update app.py

app.py

@@ -1,65 +1,66 @@
-import streamlit as st  # Correct import
-import fitz  # PyMuPDF = read the contents of the pdf file
+import streamlit as st
+import fitz  # PyMuPDF
 import openai
-from fpdf import FPDF  # Library for generating pdf files
+from fpdf import FPDF
 import os
 import tempfile
 
-# function to extract pdf file
+# Function to extract text from a PDF file
 def extract_text_from_pdf(pdf_file):
+    # Save the uploaded file to a temporary location
     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
     temp_file.write(pdf_file.read())
-    temp_file.close()
+    temp_file.close()  # Close the file to ensure it's saved
 
+    # Open the saved PDF file
     doc = fitz.open(temp_file.name)
     text = ""
-
     for page_num in range(len(doc)):
         page = doc.load_page(page_num)
         text += page.get_text()
 
+    # Delete the temporary file after reading (clean up)
     os.remove(temp_file.name)
-    return text  # Ensure this function returns text
 
-# function to ensure if it ends properly
+    return text
+
+# Function to ensure the summary ends with a full stop
 def ensure_full_stop(text):
     text = text.strip()
     if not text.endswith(('.', '!', '?')):
         text += '.'
     return text
 
-# function to summarize
+# Function to summarize text using OpenAI GPT model
 def summarize_text(api_key, text):
     openai.api_key = api_key
     response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": f"Summarize the following text:\n\n{text}"}
-        ],
-        max_tokens=2048,
+        model="gpt-3.5-turbo",  # Use "gpt-4" if you have access
+        messages=[{"role": "system", "content": "You are a helpful assistant."},
+                  {"role": "user", "content": f"Summarize the following text:\n\n{text}"}],
+        max_tokens=500,
         temperature=0.5
     )
-
     summary = response.choices[0].message['content'].strip()
     return ensure_full_stop(summary)
 
-# function to predict topic
+# Function to predict the main topic of the text
 def predict_topic(api_key, text):
     openai.api_key = api_key
     response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
+        model="gpt-3.5-turbo",  # Use "gpt-4" if you have access
         messages=[{"role": "system", "content": "You are a helpful assistant."},
                   {"role": "user", "content": f"What is the main topic of the following text?\n\n{text}"}],
         max_tokens=500,
         temperature=0.5
     )
-    return response.choices[0].message['content'].strip()
+    topic = response.choices[0].message['content'].strip()
+    return topic
 
-# Function to generate a PDF
+# Function to generate a PDF with summary and topic
 def create_pdf(summary, topic, original_file_name):
-    base_name = os.path.splitext(original_file_name)[0]
-    pdf_file_name = f"{base_name}_summary.pdf"
+    base_name = os.path.splitext(original_file_name)[0]  # Remove the .pdf extension
+    pdf_file_name = f"{base_name} summary.pdf"  # Create the new filename
 
     pdf = FPDF()
     pdf.add_page()
@@ -71,30 +72,40 @@ def create_pdf(summary, topic, original_file_name):
     pdf.cell(200, 10, txt="Predicted Main Topic", ln=True, align='C')
     pdf.multi_cell(0, 10, txt=topic)
 
+    # Save the PDF to a file in memory
     pdf_file_path = f"/tmp/{pdf_file_name}"
     pdf.output(pdf_file_path)
 
     return pdf_file_path
 
 # Streamlit UI
-st.title("Research Paper Summary")
+st.title("Research Paper Summarizer")
+
+# API Key input
 api_key = st.text_input("Enter your OpenAI API Key:", type="password")
 
+# File upload
 uploaded_file = st.file_uploader("Upload your research paper (PDF)", type=["pdf"])
 
 if uploaded_file is not None:
+    # Extract text from the uploaded PDF
     text = extract_text_from_pdf(uploaded_file)
 
     if len(text) > 1000:
+        # Summarize the text
         summary = summarize_text(api_key, text)
+        
+        # Predict the main topic
         topic = predict_topic(api_key, text)
 
+        # Display the results
         st.subheader("Summary")
         st.write(summary)
-
-        st.subheader("Predicted Topic")
+        
+        st.subheader("Predicted Main Topic")
         st.write(topic)
 
+        # Button to download results as a PDF
         if st.button("Get the Summary PDF"):
             pdf_path = create_pdf(summary, topic, uploaded_file.name)
             st.download_button(
@@ -106,4 +117,4 @@ if uploaded_file is not None:
     else:
         st.warning("The document is too short for meaningful analysis.")
 else:
-    st.info("Please upload a valid PDF file to proceed.")
+    st.info("Please upload a valid PDF file to proceed.")