import os
import gradio as gr
from PyPDF2 import PdfReader
import requests
from dotenv import load_dotenv
from transformers import AutoTokenizer
# Load environment variables
load_dotenv()
# Get the Hugging Face API token
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
def count_tokens(text):
   return len(tokenizer.encode(text))
def summarize_text(text, instructions, agent_name, max_length, temperature, repetition_penalty, top_p):
   print(f"{agent_name}: Starting summarization")
   API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
   headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
   summaries = []
   current_text = text
   while len(current_text) > 0:
       payload = {
           "inputs": f"{instructions}\n\nText to summarize:\n{current_text}",
           "parameters": {
               "max_length": max_length,
               "temperature": temperature,
               "repetition_penalty": repetition_penalty,
               "top_p": top_p
           }
       }
       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")
       generated_text = response.json()[0]["generated_text"]
       # Split the generated text by the delimiter "\n\n" and take the last part as the summary
       summary = generated_text.split("\n\n")[-1]
       summaries.append(summary)
       # Remove the summarized part from the current text
       current_text = current_text[len(summary):].strip()
   # Join all partial summaries into a final summary
   final_summary = "\n\n".join(summaries)
   return final_summary
def process_pdf(pdf_file, chunk_instructions, window_instructions, final_instructions, max_length, temperature, repetition_penalty, top_p):
   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", max_length, temperature, repetition_penalty, top_p)
       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: {count_tokens(concatenated_summary)} tokens)")
   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}", max_length, temperature, repetition_penalty, top_p)
       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", max_length, temperature, repetition_penalty, top_p)
   print("Agent 2: Finished final summarization")
   return final_summary
def pdf_summarizer(pdf_file, chunk_instructions, window_instructions, final_instructions, max_length, temperature, repetition_penalty, top_p):
   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, max_length, temperature, repetition_penalty, top_p)
       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"),
       gr.Slider(label="Max Length", minimum=500, maximum=3500, step=100, value=2000),
       gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, step=0.1, value=0.7),
       gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.1, value=1.1),
       gr.Slider(label="Top P", minimum=0.1, maximum=1.0, step=0.1, value=0.9)
   ],
   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()