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import os |
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import tkinter as tk |
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from tkinter import filedialog |
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from crewai import Agent, Task, Crew |
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from langchain_openai import ChatOpenAI |
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from langchain_community.llms import Ollama |
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from langchain.agents.agent_types import AgentType |
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from langchain_experimental.agents.agent_toolkits import create_csv_agent |
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from langchain_openai import ChatOpenAI, OpenAI |
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from langchain_google_genai import ChatGoogleGenerativeAI |
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from langchain_groq import ChatGroq |
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from crewai.process import Process |
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import gradio as gr |
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import numpy as np |
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api_key=os.getenv("GOOGLE_API_KEY") |
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os.environ["GOOGLE_API_KEY"] = api_key |
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from crewai_tools import PDFSearchTool |
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from crewai_tools import FileReadTool |
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from crewai_tools import DOCXSearchTool |
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from crewai_tools import TXTSearchTool |
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from crewai_tools import CSVSearchTool |
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llm = ChatGoogleGenerativeAI( |
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model="gemini-1.5-flash-latest", |
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verbose=True, |
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temperature=0.6, |
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google_api_key=api_key |
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) |
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class agentCollection: |
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def agentPDF(filepath): |
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agentpdf = Agent( |
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role="PDF Content Searcher and Writer", |
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goal="Generate a detailed description of relevant content from a PDF provided by the user", |
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backstory="You are an expert in navigating and extracting detailed information from PDF documents. Your task is to find the most relevant and accurate content within the PDF and provide a detailed description that addresses the user's query.", |
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verbose=True, |
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tools=[toolsCollection.toolPDF(filepath)], |
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llm=llm, |
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allow_delegation=False, |
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max_Iter=6 |
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) |
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return agentpdf |
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def agentFile(filepath): |
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agentfile = Agent( |
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role="General File Content Searcher and Writer", |
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goal="Generate a detailed description of relevant content from various file formats provided by the user", |
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backstory="You have extensive experience in handling different types of files, including PDFs, DOCX, TXT, and CSV. Your role is to expertly extract and describe the most pertinent information from any file format to meet the user's needs.", |
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verbose=True, |
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tools=[toolsCollection.toolFile(filepath)], |
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llm=llm, |
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allow_delegation=False |
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) |
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return agentfile |
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def agentTXT(filepath): |
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agenttxt = Agent( |
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role="Text File Content Searcher and Writer", |
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goal="Generate a detailed description of relevant content from text files provided by the user", |
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backstory="You specialize in working with plain text files. Your job is to sift through the text and identify the most relevant information, providing a detailed description that fulfills the user's query.", |
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verbose=True, |
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tools=[toolsCollection.toolTXT(filepath)], |
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llm=llm, |
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allow_delegation=False |
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) |
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return agenttxt |
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def agentDOCX(filepath): |
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agentdoc = Agent( |
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role="DOCX Content Searcher and Writer", |
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goal="Generate a detailed description of relevant content from DOCX files provided by the user", |
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backstory="You are proficient in reading and extracting detailed information from DOCX documents. Your expertise allows you to locate and describe the most relevant content within a DOCX file, ensuring the user's query is answered thoroughly and accurately.", |
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verbose=True, |
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tools=[toolsCollection.toolDOCX(filepath)], |
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llm=llm, |
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allow_delegation=False |
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) |
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return agentdoc |
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def agentCSV(filepath): |
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agentcsv = create_csv_agent( |
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llm, |
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filepath, |
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verbose=True, |
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agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION |
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) |
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return agentcsv |
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def agentContentWriter(): |
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agentwriter = Agent( |
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role="Content Writer", |
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goal="Summarize the data received from other agents into a comprehensive report or blog", |
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backstory="""You are a skilled content writer with expertise in synthesizing information from various sources. Your task is to use the detailed descriptions provided by other agents to create a well-structured and coherent summary that addresses the user's query in detail.""", |
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verbose=True, |
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llm=llm, |
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max_Iter=10 |
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) |
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return agentwriter |
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class toolsCollection: |
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def toolPDF(filepath): |
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if filepath == "": |
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print("FILE NOT FOUND") |
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return |
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pdftool = PDFSearchTool( |
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config=dict( |
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llm=dict( |
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provider="google", |
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config=dict( |
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model="gemini-1.5-flash-latest", |
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), |
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), |
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embedder=dict( |
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provider="huggingface", |
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config=dict( |
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model="sentence-transformers/msmarco-distilbert-base-v4" |
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), |
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), |
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), |
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pdf=filepath |
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) |
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return pdftool |
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def toolFile(filepath): |
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filetool = FileReadTool( |
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config=dict( |
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llm=dict( |
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provider="google", |
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config=dict( |
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model="gemini-1.5-flash-latest", |
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), |
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), |
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embedder=dict( |
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provider="huggingface", |
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config=dict( |
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model="sentence-transformers/msmarco-distilbert-base-v4" |
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), |
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), |
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), |
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file_path=filepath |
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) |
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return filetool |
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def toolTXT(filepath): |
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txttool = TXTSearchTool( |
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config=dict( |
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llm=dict( |
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provider="google", |
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config=dict( |
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model="gemini-1.5-flash-latest", |
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), |
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), |
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embedder=dict( |
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provider="huggingface", |
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config=dict( |
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model="sentence-transformers/msmarco-distilbert-base-v4" |
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), |
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), |
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), |
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txt=filepath |
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) |
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return txttool |
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def toolDOCX(filepath): |
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if filepath == "": |
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print("FILE NOT FOUND") |
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return |
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docxtool = DOCXSearchTool( |
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config=dict( |
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llm=dict( |
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provider="google", |
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config=dict( |
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model="gemini-1.5-flash-latest", |
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), |
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), |
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embedder=dict( |
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provider="huggingface", |
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config=dict( |
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model="sentence-transformers/msmarco-distilbert-base-v4" |
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), |
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), |
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), |
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docx=filepath |
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) |
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return docxtool |
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def toolCSV(filepath): |
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csvtool = CSVSearchTool( |
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config=dict( |
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llm=dict( |
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provider="google", |
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config=dict( |
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model="gemini-1.5-flash-latest", |
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), |
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), |
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embedder=dict( |
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provider="huggingface", |
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config=dict( |
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model="sentence-transformers/msmarco-distilbert-base-v4" |
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), |
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), |
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), |
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csv=filepath |
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) |
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return csvtool |
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def run_ai(file, query, required_ans_format): |
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filepath = file.name |
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if filepath.endswith(".pdf"): |
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myagent = agentCollection.agentPDF(filepath) |
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elif filepath.endswith(".json"): |
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myagent = agentCollection.agentFile(filepath) |
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elif filepath.endswith(".docx"): |
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myagent = agentCollection.agentDOCX(filepath) |
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elif filepath.endswith(".txt"): |
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myagent = agentCollection.agentTXT(filepath) |
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elif filepath.endswith(".csv"): |
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myagent = agentCollection.agentCSV(filepath) |
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return myagent.run(query) |
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task = Task( |
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description=f"{query}", |
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expected_output=f"detailed description on {query}", |
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agent=myagent, |
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) |
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content_writer_agent = agentCollection.agentContentWriter() |
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content_writer_task = Task( |
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description=f"{query}", |
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expected_output=f'{required_ans_format}', |
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agent=content_writer_agent, |
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) |
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crew = Crew( |
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agents=[myagent, content_writer_agent], |
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tasks=[task, content_writer_task], |
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process=Process.sequential, |
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verbose=2 |
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) |
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result = crew.kickoff() |
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return result |
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interface = gr.Interface( |
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fn=run_ai, |
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inputs=[ |
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gr.File(label="Upload File"), |
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gr.Textbox(label="Query"), |
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gr.Textbox(label="Expected Output") |
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], |
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outputs="text", |
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title="DocuSmart", |
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description=( |
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"Upload a file (CSV, PDF, DOCX, TXT, JSON) and enter your query to get detailed information.\n\n" |
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"### Instructions:\n" |
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"1. Upload the file you want to talk to.\n" |
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"2. Enter your question in the Query field.\n" |
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"3. Specify the desired output format, e.g., one line answer.\n" |
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"4. Press 'Submit' and wait for the response.\n\n" |
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), |
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examples=[ |
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["LabManual.pdf", "What is RIP?", "detailed description"], |
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["ElectricCarData_Clean.csv", "Which Brand has most vehicles?", "one line answer"] |
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], |
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theme=gr.themes.Soft() |
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) |
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interface.launch() |
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