Create app.py

app.py

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+import streamlit as st
+import fitz  # PyMuPDF
+import openai
+from fpdf import FPDF
+import os
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        text += page.get_text()
+    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 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",  # 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 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",  # 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
+    )
+    topic = response.choices[0].message['content'].strip()
+    return topic
+
+# 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]  # Remove the .pdf extension
+    pdf_file_name = f"{base_name} summary.pdf"  # Create the new filename
+
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+
+    pdf.cell(200, 10, txt="Summary", ln=True, align='C')
+    pdf.multi_cell(0, 10, txt=summary)
+
+    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 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 Main Topic")
+        st.write(topic)
+
+        # Button to download results as a PDF
+        if st.button("Download Summary as 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.")