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README.md

@@ -1,8 +1,8 @@
 ---
-title: PDF Summarizer
-emoji: 🌖
-colorFrom: pink
-colorTo: indigo
+title: DocsSummarizer
+emoji: 🚀
+colorFrom: indigo
+colorTo: green
 sdk: streamlit
 sdk_version: 1.38.0
 app_file: app.py

app (1).py

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+from dotenv import load_dotenv
+import os
+import fitz  # PyMuPDF
+import nltk
+from reportlab.lib.pagesizes import letter
+from reportlab.lib import colors
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.units import inch
+import streamlit as st
+from groq import Groq
+load_dotenv()
+# Download NLTK resources
+nltk.download('punkt')
+
+# Initialize Groq Client using the environment variable
+client = Groq(
+    api_key=os.getenv('GROQ_API_KEY')
+)
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Function to segment text into topics
+def segment_text_into_topics(text):
+    topics = text.split('\n\n')  # Simple split by double newline; can be customized
+    return topics
+
+# Function to summarize text using LLM
+def summarize_text(topic):
+    prompt = f"Summarize the following text and define any technical terms used. Provide clear and contextually relevant definitions for the terms, especially those related to AI and machine learning:\n\n{topic}"
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an expert summarizer and technical writer who provides concise and clear summaries of topics, and defines any technical terms with relevance to the context."
+                },
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
+            ],
+            model="llama-3.1-70b-versatile",
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Function to define technical terms using LLM
+def define_technical_terms(terms):
+    definitions = {}
+    for term in terms:
+        prompt = f"Define the technical term '{term}' in the context of AI and machine learning."
+        try:
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {
+                        "role": "system",
+                        "content": "You are an expert in AI and machine learning. Provide clear and contextually relevant definitions for technical terms."
+                    },
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                model="llama-3.1-70b-versatile",
+            )
+            definitions[term] = chat_completion.choices[0].message.content.strip()
+        except Exception as e:
+            definitions[term] = f"Definition not found due to an error: {str(e)}"
+    return definitions
+
+# Function to process the entire PDF and generate summaries
+def process_pdf(pdf_file):
+    text = extract_text_from_pdf(pdf_file)
+    topics = segment_text_into_topics(text)
+    summary_output = ""
+    
+    for topic in topics:
+        summary = summarize_text(topic)
+        summary_output += f"Summary:\n{summary}\n\n"
+
+        # Extract and define technical terms
+        words = set(topic.split())
+        technical_terms = [word for word in words if word.isalpha() and word.isupper()]
+        if technical_terms:
+            definitions = define_technical_terms(technical_terms)
+            summary_output += "Technical Terms and Definitions:\n"
+            for term, definition in definitions.items():
+                summary_output += f"{term}: {definition}\n"
+            summary_output += "\n"
+
+    return summary_output
+
+# Function to create a PDF from the summary with improved formatting
+def create_summary_pdf(output_text, output_pdf_path):
+    doc = SimpleDocTemplate(output_pdf_path, pagesize=letter)
+    story = []
+
+    # Define styles
+    styles = getSampleStyleSheet()
+    heading_style = styles['Heading1']
+    subheading_style = styles['Heading2']
+    para_style = styles['BodyText']
+    tech_term_style = ParagraphStyle(
+        'TechTerm',
+        parent=styles['BodyText'],
+        textColor=colors.blue,
+        spaceBefore=10,
+        leftIndent=20
+    )
+
+    # Process the text for PDF
+    lines = output_text.split('\n\n')
+    for line in lines:
+        if line.startswith("Summary:"):
+            title = line.split(":", 1)[1].strip()
+            story.append(Paragraph("Summary", subheading_style))
+            story.append(Spacer(1, 0.1 * inch))
+            story.append(Paragraph(title, para_style))
+            story.append(Spacer(1, 0.2 * inch))
+        elif "Technical Terms and Definitions:" in line:
+            story.append(Paragraph("Technical Terms and Definitions", subheading_style))
+            story.append(Spacer(1, 0.1 * inch))
+            terms = line.split("\n")[1:]
+            for term in terms:
+                story.append(Paragraph(term, tech_term_style))
+                story.append(Spacer(1, 0.1 * inch))
+        else:
+            story.append(Paragraph(line, para_style))
+            story.append(Spacer(1, 0.2 * inch))
+
+    doc.build(story)
+
+# Streamlit Interface
+st.title("PDF Summarizer with Technical Definitions")
+
+uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+
+if uploaded_file is not None:
+    st.write("Processing...")
+    summary = process_pdf(uploaded_file)
+    
+    output_pdf_path = "summary_output.pdf"
+    create_summary_pdf(summary, output_pdf_path)
+    
+    with open(output_pdf_path, "rb") as file:
+        btn = st.download_button(
+            label="Download Summary PDF",
+            data=file,
+            file_name="summary_output.pdf",
+            mime="application/pdf"
+        )

env

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+GROQ_API_KEY = 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

gitignore

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+.env

requirements.txt

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+pymupdf
+nltk
+reportlab
+requests
+groq
+streamlit
+python-dotenv