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import os
import gradio as gr
from PyPDF2 import PdfReader
import requests
from dotenv import load_dotenv
import tiktoken
# Load environment variables
load_dotenv()
# Get the Hugging Face API token
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
# Initialize the tokenizer
tokenizer = tiktoken.get_encoding("cl100k_base")
def count_tokens(text):
return len(tokenizer.encode(text))
def summarize_text(text, instructions, agent_name):
print(f"{agent_name}: Starting summarization")
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x22B-Instruct-v0.1"
headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
payload = {
"inputs": f"{instructions}\n\nText to summarize:\n{text}",
"parameters": {"max_length": 500}
}
print(f"{agent_name}: Sending request to API")
response = requests.post(API_URL, headers=headers, json=payload)
print(f"{agent_name}: Received response from API")
return response.json()[0]["generated_text"]
def process_pdf(pdf_file, chunk_instructions, window_instructions, final_instructions):
print("Starting PDF processing")
# Read PDF
reader = PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text() + "\n\n"
print(f"Extracted {len(reader.pages)} pages from PDF")
# Chunk the text (simple splitting by pages for this example)
chunks = text.split("\n\n")
print(f"Split text into {len(chunks)} chunks")
# Agent 1: Summarize each chunk
agent1_summaries = []
for i, chunk in enumerate(chunks):
print(f"Agent 1: Processing chunk {i+1}/{len(chunks)}")
summary = summarize_text(chunk, chunk_instructions, "Agent 1")
agent1_summaries.append(summary)
print("Agent 1: Finished processing all chunks")
# Concatenate Agent 1 summaries
concatenated_summary = "\n\n".join(agent1_summaries)
print(f"Concatenated Agent 1 summaries (length: {len(concatenated_summary)})")
print(f"Concatenated Summary:{concatenated_summary}")
# Sliding window approach
window_size = 3500 # in tokens
step_size = 3000 # overlap of 500 tokens
windows = []
current_position = 0
while current_position < len(concatenated_summary):
window_end = current_position
window_text = ""
while count_tokens(window_text) < window_size and window_end < len(concatenated_summary):
window_text += concatenated_summary[window_end]
window_end += 1
windows.append(window_text)
current_position += step_size
print(f"Created {len(windows)} windows for intermediate summarization")
# Intermediate summarization
intermediate_summaries = []
for i, window in enumerate(windows):
print(f"Processing window {i+1}/{len(windows)}")
summary = summarize_text(window, window_instructions, f"Window {i+1}")
intermediate_summaries.append(summary)
# Final summarization
final_input = "\n\n".join(intermediate_summaries)
print(f"Final input length: {count_tokens(final_input)} tokens")
final_summary = summarize_text(final_input, final_instructions, "Agent 2")
print("Agent 2: Finished final summarization")
return final_summary
def pdf_summarizer(pdf_file, chunk_instructions, window_instructions, final_instructions):
if pdf_file is None:
print("Error: No PDF file uploaded")
return "Please upload a PDF file."
try:
print(f"Starting summarization process for file: {pdf_file.name}")
summary = process_pdf(pdf_file.name, chunk_instructions, window_instructions, final_instructions)
print("Summarization process completed successfully")
return summary
except Exception as e:
print(f"An error occurred: {str(e)}")
return f"An error occurred: {str(e)}"
# Gradio interface
iface = gr.Interface(
fn=pdf_summarizer,
inputs=[
gr.File(label="Upload PDF"),
gr.Textbox(label="Chunk Instructions", placeholder="Instructions for summarizing each chunk"),
gr.Textbox(label="Window Instructions", placeholder="Instructions for summarizing each window"),
gr.Textbox(label="Final Instructions", placeholder="Instructions for final summarization")
],
outputs=gr.Textbox(label="Summary"),
title="PDF Earnings Summary Generator",
description="Upload a PDF of an earnings summary or transcript to generate a concise summary."
)
print("Launching Gradio interface")
iface.launch() |