app.py

import os
import fitz  # PyMuPDF
import gradio as gr
import autogen
from autogen.agentchat.contrib.capabilities import transform_messages, transforms
from autogen.agentchat.contrib.capabilities.text_compressors import LLMLingua
from autogen.agentchat.contrib.capabilities.transforms import TextMessageCompressor
import mysql.connector

import copy
import pprint
import re
from typing import Dict, List, Tuple

# MySQLに接続
conn = mysql.connector.connect(
    host="www.ryhintl.com",
    user="smairuser",
    password="smairuser",
    port=36000,
    database="smair"
)

# カーソルを取得
cursor = conn.cursor(dictionary=True)

# List API Keys
select_one_data_query = "SELECT * FROM agentic_apis"
cursor.execute(select_one_data_query)
result = cursor.fetchall()
# JSONをパースしてkeyを抽出
keys = [item['key'] for item in result]

os.environ["GROQ_API_KEY"] = keys[2]

def extract_text_from_pdf(file):
    text = ""
    with fitz.open("./"+file) as doc:
        for page in doc:
            text += page.get_text()

    return text

def summarize_pdf(file):
    pdf_text = extract_text_from_pdf(file)
    #pdf_text = extract_text_from_pdf(text)
    
    #system_message = "You are a world class researcher."
    system_message = "貴方は世界的なレベルのリサーチャーです。"
    config_list = [{
            "model": "llama-3.3-70b-versatile",
            #"model": "llama-guard-3-8b",
            "api_key": os.environ.get("GROQ_API_KEY"),
            "api_type": "groq"
        }]
    
    researcher = autogen.ConversableAgent(
        "assistant",
        llm_config={"config_list": config_list},
        max_consecutive_auto_reply=1,
        system_message=system_message,
        human_input_mode="NEVER",
    )
    user_proxy = autogen.UserProxyAgent(
        "user_proxy",
        human_input_mode="NEVER",
        is_termination_msg=lambda x: "TERMINATE" in x.get("content", ""),
        max_consecutive_auto_reply=1,
        code_execution_config={"work_dir":"coding", "use_docker":False}
    )
    
    llm_lingua = LLMLingua()
    #text_compressor = TextMessageCompressor(text_compressor=llm_lingua,compression_params={"target_token": 13000},cache=None)
    text_compressor = TextMessageCompressor(text_compressor=llm_lingua,compression_params={"target_token": 5999},cache=None)
    compressed_text = text_compressor.apply_transform([{"content": pdf_text}])
    
    
    #content_list = [item['content'] for item in compressed_text]
    # Limit the message history to the 3 most recent messages
    max_msg_transfrom = transforms.MessageHistoryLimiter(max_messages=3)

    # Limit the token limit per message to 10 tokens
    token_limit_transform = transforms.MessageTokenLimiter(max_tokens_per_message=3, min_tokens=10)
    
    '''messages = [
        {"role": "user", "content": "hello"},
        {"role": "assistant", "content": [{"type": "text", "text": "there"}]},
        {"role": "user", "content": "how"},
        {"role": "assistant", "content": [{"type": "text", "text": "are you doing?"}]},
        {"role": "user", "content": "very very very very very very long string"},
    ]

    processed_messages = max_msg_transfrom.apply_transform(copy.deepcopy(messages))
    pprint.pprint(processed_messages)

    processed_messages = token_limit_transform.apply_transform(copy.deepcopy(messages))

    pprint.pprint(processed_messages)'''
    

    context_handling = transform_messages.TransformMessages(
        transforms=[
            transforms.MessageHistoryLimiter(max_messages=10),
            transforms.MessageTokenLimiter(max_tokens=6000, max_tokens_per_message=2000, min_tokens=500),
            #transforms.MessageTokenLimiter(max_tokens=1000, max_tokens_per_message=50, min_tokens=500),
        ]
    )

    context_handling.add_to_agent(researcher)
    
    
    #context_handling = transform_messages.TransformMessages(transforms=[text_compressor])
    #context_handling.add_to_agent(researcher)
    
    #message = "Summarize this research paper for me in Japanese, include the important information" + pdf_text
    message = "この資料を日本語で要約し、重要な情報を含めてください。節約されたトークン数も表示してください。" + pdf_text
    result = user_proxy.initiate_chat(recipient=researcher, clear_history=True, message=message, silent=True)
    
    #print(text_compressor.get_logs([], []))
    # タプルの例
    tresult = text_compressor.get_logs([], [])
    #print(tresult)

    # 文字列から数字を抽出
    #saved_tokens = str(int(tresult[0].split()[0]))
    #print(saved_tokens)
    
    return result.chat_history[1]["content"]
    #return result.chat_history[1]["content"]+"\n\n"+saved_tokens+"トークンが節約できました。"

iface = gr.Interface(
    fn=summarize_pdf,
    #inputs=gr.inputs.File(label="Upload PDF"),
    inputs=gr.Dropdown(
        choices=["yoin.pdf", "spo_revenue.pdf", "lings.pdf", "korea-ai.pdf"],  # ドロップダウンの選択肢を指定
        label="PDFを選択"  # ラベルを指定
    ),
    outputs="text",
    title="Research Paper Summarizer",
    description="Select a PDF and get a summary in Japanese."
)

iface.launch()