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| 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, agent_name): | |
| print(f"{agent_name}: Starting summarization") | |
| 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} | |
| } | |
| 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, 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)})") | |
| # Agent 2: Final summarization | |
| print("Agent 2: Starting final summarization") | |
| final_summary = summarize_text(concatenated_summary, final_instructions, "Agent 2") | |
| print("Agent 2: Finished final summarization") | |
| return final_summary | |
| def pdf_summarizer(pdf_file, chunk_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, 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="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() |