import os import gradio as gr from PyPDF2 import PdfReader import requests from dotenv import load_dotenv # Load environment variables load_dotenv() # Get the Hugging Face API token HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN") def summarize_text(text, instructions): API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct" headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"} payload = { "inputs": f"{instructions}\n\nText to summarize:\n{text}", "parameters": {"max_length": 500} } response = requests.post(API_URL, headers=headers, json=payload) return response.json()[0]["generated_text"] def process_pdf(pdf_file, chunk_instructions, final_instructions): # Read PDF reader = PdfReader(pdf_file) text = "" for page in reader.pages: text += page.extract_text() + "\n\n" # Chunk the text (simple splitting by pages for this example) chunks = text.split("\n\n") # Agent 1: Summarize each chunk agent1_summaries = [] for chunk in chunks: summary = summarize_text(chunk, chunk_instructions) agent1_summaries.append(summary) # Concatenate Agent 1 summaries concatenated_summary = "\n\n".join(agent1_summaries) # Agent 2: Final summarization final_summary = summarize_text(concatenated_summary, final_instructions) return final_summary def pdf_summarizer(pdf_file, chunk_instructions, final_instructions): if pdf_file is None: return "Please upload a PDF file." try: summary = process_pdf(pdf_file.name, chunk_instructions, final_instructions) return summary except Exception as 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="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." ) iface.launch()