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import gradio as gr
from langchain_core.prompts import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain_community.document_loaders import PyPDFLoader
from langchain_openai import ChatOpenAI
from langchain_community.callbacks import get_openai_callback
import os
from dotenv import load_dotenv
os.makedirs("data", exist_ok=True)
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
def summarize_pdf(pdf_file, custom_prompt="", openai_api_key=None):
"""
Summarizes the content of a PDF file using a custom prompt.
Args:
pdf_file (UploadedFile): The uploaded PDF file.
custom_prompt (str): The prompt for summarization.
openai_api_key (str, optional): User-provided OpenAI API key.
Returns:
tuple: Summary in markdown format and the cost in USD.
"""
pdf_path = os.path.join("data", "tmp.pdf")
with open(pdf_path, "wb") as f:
f.write(pdf_file)
api_key = openai_api_key if openai_api_key else OPENAI_API_KEY
if not api_key:
return "Error: No OpenAI API key provided.", "N/A"
with get_openai_callback() as cb:
try:
model = ChatOpenAI(
model="gpt-4o-mini",
temperature=0,
openai_api_key=api_key
)
loader = PyPDFLoader(pdf_path)
docs = loader.load_and_split()
if not custom_prompt.strip():
custom_prompt = default_prompt
prompt_template = (
custom_prompt
+ """
{text}
SUMMARY:"""
)
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(
model,
chain_type="map_reduce",
map_prompt=PROMPT,
combine_prompt=PROMPT
)
summary = chain({"input_documents": docs}, return_only_outputs=True)["output_text"]
total_cost = cb.total_cost
return summary, f"${total_cost:.4f}"
except Exception as e:
return f"An error occurred: {str(e)}", "N/A"
default_prompt = (
"Summarize this paper. Return markdown, keep it in a language that scientists understand, "
"but the purpose is to highlight the key takeaways, so that we save time for the reader."
)
with gr.Blocks() as demo:
gr.Markdown("# PDF Summarizer 📝")
gr.Markdown("Upload a PDF, customize your summarization prompt, and get a concise summary along with the processing cost.")
with gr.Row():
with gr.Column():
if OPENAI_API_KEY is None:
api_key_input = gr.Textbox(
label="OpenAI API Key",
type="password",
placeholder="Enter your OpenAI API key."
)
else:
api_key_input = gr.Textbox(
label="OpenAI API Key (Optional)",
type="password",
placeholder="Enter your OpenAI API key if you want to override the global key."
)
prompt_input = gr.Textbox(
label="Custom Prompt",
lines=4,
value=default_prompt,
placeholder="Enter your custom summarization prompt here..."
)
pdf_input = gr.File(
label="Upload PDF",
type="binary",
file_types=[".pdf"],
)
summarize_btn = gr.Button("Summarize")
with gr.Column():
cost_output = gr.Textbox(label="Approximate Cost (USD)", interactive=False)
summary_output = gr.Markdown(label="Summary")
summarize_btn.click(
fn=summarize_pdf,
inputs=[pdf_input, prompt_input, api_key_input],
outputs=[summary_output, cost_output]
)
gr.Markdown("---")
gr.Markdown("Created by [Daniel Herman](https://www.hermandaniel.com)")
if __name__ == "__main__":
demo.launch() |