import os
import gradio as gr
from langchain_groq import ChatGroq
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
import PyPDF2
from langchain.prompts import PromptTemplate
# Set up API keys
groq_api_key = os.environ.get('GROQ_API_KEY')
# Set up LLM
llm = ChatGroq(temperature=0, model_name='llama-3.1-8b-instant', groq_api_key=groq_api_key)
def extract_text_from_pdf(pdf_file):
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
def chunk_text(text):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=4000,
chunk_overlap=400,
length_function=len
)
chunks = text_splitter.split_text(text)
return [Document(page_content=chunk) for chunk in chunks]
def summarize_chunks(chunks, conciseness):
# Adjust the prompts based on the conciseness level
map_prompt_template = f"""Write a {'very concise' if conciseness > 0.7 else 'detailed'} summary of the following text, focusing on the {'most crucial' if conciseness > 0.7 else 'key'} points:
"{{text}}"
{'CONCISE' if conciseness > 0.7 else 'DETAILED'} SUMMARY:"""
combine_prompt_template = f"""Write a {'highly condensed' if conciseness > 0.7 else 'comprehensive'} summary of the following text, capturing the {'essential' if conciseness > 0.7 else 'key'} points and main ideas:
"{{text}}"
{'CONDENSED' if conciseness > 0.7 else 'COMPREHENSIVE'} SUMMARY:"""
map_prompt = PromptTemplate(template=map_prompt_template, input_variables=["text"])
combine_prompt = PromptTemplate(template=combine_prompt_template, input_variables=["text"])
# Adjust the chain type based on the document length and conciseness
total_length = sum(len(chunk.page_content) for chunk in chunks)
if total_length < 10000 or conciseness > 0.8:
chain = load_summarize_chain(
llm,
chain_type="stuff",
prompt=combine_prompt
)
else:
chain = load_summarize_chain(
llm,
chain_type="map_reduce",
map_prompt=map_prompt,
combine_prompt=combine_prompt,
verbose=True
)
summary = chain.run(chunks)
return summary
def summarize_content(pdf_file, text_input, conciseness):
if pdf_file is None and not text_input:
return "Please upload a PDF file or enter text to summarize."
if pdf_file is not None:
# Extract text from PDF
text = extract_text_from_pdf(pdf_file)
else:
# Use the input text
text = text_input
# Chunk the text
chunks = chunk_text(text)
# Summarize chunks with conciseness level
final_summary = summarize_chunks(chunks, conciseness)
return final_summary
with gr.Blocks(theme=gr.themes.Soft()) as iface:
gr.Markdown(
"""
PDF And Text Summarizer
Advanced PDF and Text Summarization with Conciseness Control - Upload your PDF document or enter text directly, adjust the conciseness level, and let AI generate a summary.
"""
)
with gr.Row():
with gr.Column(scale=1):
input_pdf = gr.File(label="Upload PDF (optional)", file_types=[".pdf"])
input_text = gr.Textbox(label="Or enter text here", lines=5, placeholder="Paste or type your text here...")
conciseness_slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.1, label="Conciseness Level")
submit_btn = gr.Button("Generate Summary", variant="primary")
with gr.Column(scale=2):
output = gr.Textbox(label="Generated Summary", lines=10)
gr.Markdown(
"""
### How it works
1. Upload a PDF file or enter text directly
2. Adjust the conciseness level:
- 0 (Most detailed) to 1 (Most concise)
3. Click "Generate Summary"
4. Wait for the AI to process and summarize your content
5. Review the generated summary
*Powered by LLAMA 3.1 8B model and LangChain*
"""
)
gr.HTML(
"""
"""
)
submit_btn.click(summarize_content, inputs=[input_pdf, input_text, conciseness_slider], outputs=output)
iface.launch()