Update app.py

app.py

@@ -1,76 +1,65 @@
-import streamlit
-import fitz           # PyMuPDF = read the contents of the pdf file
+import streamlit as st  # Correct import
+import fitz  # PyMuPDF = read the contents of the pdf file
 import openai
-from fpdf import FPDF # library for generating pdf files
+from fpdf import FPDF  # Library for generating pdf files
 import os
 import tempfile
 
-
 # function to extract pdf file
 def extract_text_from_pdf(pdf_file):
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    temp_file.write(pdf_file.read())
+    temp_file.close()
 
-  # save the uploaded file
-  temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-  temp_file.write(pdf_file.read())
-  temp_file.close() # close the file to ensure it is saved
-
-  # open the saved pdf file
-  doc = fitz.open(temp_file.name)
-  text = "" # extracted info saved here
-
-  # iterate 'doc' page wise iteration
-  for page_num in range(len(doc)):
-    page = doc.load_page(page_num)
-    text += page.get_text() # saving the text of each page into the 'text'
+    doc = fitz.open(temp_file.name)
+    text = ""
 
-  # delete the temp file after reading
-  os.remove(temp_file.name)
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        text += page.get_text()
 
+    os.remove(temp_file.name)
+    return text  # Ensure this function returns text
 
-
-# function to ensure if it is ends properly
-# it might happen that LLM text generation might end in between, at this point we require this function
+# function to ensure if it ends properly
 def ensure_full_stop(text):
-  text = text.strip()
-  if not text.endswith(('.', '!', '?')):
-    text += '.'
-  return text
-
+    text = text.strip()
+    if not text.endswith(('.', '!', '?')):
+        text += '.'
+    return text
 
 # function to summarize
 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,
-      temp = 0.5 # creativity, 0.5 is sweet spot
-  )
-
-  summary = response.choices[0].message['content'].strip()
-  return ensure_full_stop(summary)
-
-
-# understand the main gist of the pdf
+    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,
+        temperature=0.5
+    )
+
+    summary = response.choices[0].message['content'].strip()
+    return ensure_full_stop(summary)
+
+# function to predict topic
 def predict_topic(api_key, text):
     openai.api_key = api_key
     response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",  # Use "gpt-4" if you have access
+        model="gpt-3.5-turbo",
         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
     )
-    topic = response.choices[0].message['content'].strip()
-    return topic
+    return response.choices[0].message['content'].strip()
 
-# Function to generate a PDF with summary and topic
+# Function to generate a PDF
 def create_pdf(summary, topic, original_file_name):
-    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
+    base_name = os.path.splitext(original_file_name)[0]
+    pdf_file_name = f"{base_name}_summary.pdf"
 
     pdf = FPDF()
     pdf.add_page()
@@ -82,45 +71,39 @@ 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
 
-st.title("Research paper summary")
-api_key = st.text_input("Enter your OpenAI API Key: ", type="password")
+# Streamlit UI
+st.title("Research Paper Summary")
+api_key = st.text_input("Enter your OpenAI API Key:", type="password")
 
-# file uploading
 uploaded_file = st.file_uploader("Upload your research paper (PDF)", type=["pdf"])
 
-if uploaded_file is not None: 
-  text = extract_text_from_pdf(uploaded_file)
-
-  if len(text) > 1000: 
-    summary = summarize_text(api_key, text)
-    topic = predict_topic(api_key, text)
-
-    st.subheader("Summary")
-    st.write(summary)
-
-    st.subheader("Predicted 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 (
-            label="Download Summary PDF",
-            data=open(pdf_path, "rb").read(),
-            file_name=os.path.basename(pdf_path),
-            mime="application/pdf"
-        )
-  else:
-    st.warning("The document is too short for meaningful analysis.")
-  
+if uploaded_file is not None:
+    text = extract_text_from_pdf(uploaded_file)
+
+    if len(text) > 1000:
+        summary = summarize_text(api_key, text)
+        topic = predict_topic(api_key, text)
+
+        st.subheader("Summary")
+        st.write(summary)
+
+        st.subheader("Predicted Topic")
+        st.write(topic)
+
+        if st.button("Get the Summary PDF"):
+            pdf_path = create_pdf(summary, topic, uploaded_file.name)
+            st.download_button(
+                label="Download Summary PDF",
+                data=open(pdf_path, "rb").read(),
+                file_name=os.path.basename(pdf_path),
+                mime="application/pdf"
+            )
+    else:
+        st.warning("The document is too short for meaningful analysis.")
 else:
     st.info("Please upload a valid PDF file to proceed.")
-
-def get_text():
-  pass