Create app.py

app.py

@@ -0,0 +1,142 @@
+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"]+"\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��
+        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()