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Delete Groq.py
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Groq.py
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import os
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import pandas as pd
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import gradio as gr
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from crewai import Agent, Task, Crew
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from langchain_openai import ChatOpenAI
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from crewai_tools import PDFSearchTool, FileReadTool, DOCXSearchTool, CSVSearchTool
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from langchain_google_genai import ChatGoogleGenerativeAI
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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_groq import ChatGroq
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# API keys-----------------move them to ENV
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os.environ["OPENAI_API_KEY"] = "NA"
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os.environ["GOOGLE_API_KEY"] = "AIzaSyD7jKc5MdkRLakxcyhvrpie8XgbwY98NMo"
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# Load The Groq model for LLM
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llm = ChatGroq(
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api_key="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",
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model="llama3-70b-8192"
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)
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#<-----------------------------Tools----------------------------------->
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class tools:
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def pdfRead(path):
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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=path
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)
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return PDFtool
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def fileRead(path):
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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=path
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)
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return Filetool
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def docsRead(path):
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Docstool = 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=path
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)
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return Docstool
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#<-----------------------------Tools----------------------------------->
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#<------------------------------Agents START------------------------->
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class AgentLoader:
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def csvReaderAgent(path):
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agent = create_csv_agent(
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ChatGoogleGenerativeAI(temperature=0.6, model="gemini-1.5-flash-latest"),
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path,
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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 agent
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def fileReaderAgent(path):
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FileReader = Agent(
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role='File searcher',
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goal='To analyse and generate optimal and reliable results',
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backstory="""You are a File specialist and can handle multiple file formats like .txt, .csv, .json etc.
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You are responsible to analyse the file to find the relevant content that solves the problem of the user and generate high quality and reliable results.
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You should also provide the results of your analysis and searching.""",
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llm=llm,
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verbose=True,
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tools=[tools.fileRead(path)],
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allow_delegation=False
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)
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return FileReader
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def PdfReaderAgent(path):
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PdfReader = Agent(
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role='PDF searcher',
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goal='To analyse and generate optimal and reliable results',
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backstory="""You are a PDF specialist and content writer.
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You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
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You should also provide the results of your analysis and searching.""",
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llm=llm,
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verbose=True,
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tools=[tools.pdfRead(path)],
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allow_delegation=False
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)
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return PdfReader
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def DocsReaderAgent(path):
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DocsReader = Agent(
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role='Docs searcher',
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goal='To analyse and generate optimal and reliable results',
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backstory="""You are a Docs specialist and content writer.
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You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
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You should also provide the results of your analysis and searching.""",
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llm=llm,
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verbose=True,
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tools=[tools.docsRead(path)],
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allow_delegation=False
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)
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return DocsReader
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def writerAgent():
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writer=Agent(
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role='Content Writer',
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goal='To provide QUICK and reliable output',
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backstory="""You are content specialist.
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You are responsible to generate high quality results in the required format very quickly as soon as data is available.
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You are very accurate and fast at the same time.""",
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verbose=True,
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llm=llm,
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max_iter=5
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)
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return writer
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#<------------------------------Agents END------------------------->
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#<-------------------------------Tasks---------------------------->
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def getTasks(query, agent, exp):
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task_read=Task(
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description=f'{query}',
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agent=agent,
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expected_output=f'A detailed information on {query}'
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)
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task_write=Task(
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description=f'{query}',
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agent=AgentLoader.writerAgent(),
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expected_output=exp
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)
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return [task_read, task_write]
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# Gradio interface function
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def process_file(file, query, expected_output):
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path = file.name
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if path.endswith(".pdf"):
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agent = AgentLoader.PdfReaderAgent(path)
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elif path.endswith(".docx"):
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agent = AgentLoader.DocsReaderAgent(path)
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elif path.endswith(".json") or path.endswith(".txt"):
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agent = AgentLoader.fileReaderAgent(path)
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elif path.endswith(".csv"):
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agent = AgentLoader.csvReaderAgent(path)
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results = agent.run(query)
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else:
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return 'File NOT supported'
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if not path.endswith(".csv"):
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task1 = getTasks(query, agent, expected_output)
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mycrew = Crew(
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agents=[agent, AgentLoader.writerAgent()],
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tasks=task1,
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verbose=True
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)
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results = mycrew.kickoff()
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return results
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# Create the Gradio interface
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interface = gr.Interface(
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fn=process_file,
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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="File Analyzer",
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description="Upload a file (CSV, PDF, DOCX, TXT, JSON) and enter your query to get detailed information."
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)
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# Launch the Gradio interface
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interface.launch()
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