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import gradio as gr
import os
from groq import Groq
from crewai import Agent, Task, Crew, Process
from langchain_community.tools import DuckDuckGoSearchRun
# Initialize the GROQ client
client = Groq(
api_key=os.environ["GROQ_API_KEY"],
)
# Initialize the search tool
search_tool = DuckDuckGoSearchRun()
# Create a function to generate text using GROQ
def generate_text(prompt, max_tokens=500):
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt,
}
],
model="mixtral-8x7b-32768",
max_tokens=max_tokens,
)
return chat_completion.choices[0].message.content
# Create agents using the GROQ text generation function
def create_agent(role, goal, backstory):
return Agent(
role=role,
goal=goal,
backstory=backstory,
verbose=True,
allow_delegation=False,
llm_config={
"function": generate_text,
"config": {
"max_tokens": 500
}
}
)
researcher = create_agent(
'Senior Researcher',
'Conduct thorough research on given topics',
'You are an experienced researcher with a keen eye for detail and the ability to find relevant information quickly.'
)
writer = create_agent(
'Content Writer',
'Create engaging and informative content based on research',
'You are a skilled writer capable of turning complex information into easily understandable and engaging content.'
)
editor = create_agent(
'Editor',
'Refine and improve the written content',
'You are a meticulous editor with a strong command of language and an eye for clarity and coherence.'
)
def create_crew(query):
# Create tasks
research_task = Task(
description=f"Research the following topic thoroughly: {query}",
agent=researcher
)
writing_task = Task(
description="Write an informative article based on the research conducted",
agent=writer
)
editing_task = Task(
description="Review and refine the written article, ensuring clarity, coherence, and engagement",
agent=editor
)
# Create the crew
crew = Crew(
agents=[researcher, writer, editor],
tasks=[research_task, writing_task, editing_task],
verbose=2,
process=Process.sequential
)
return crew
def process_query(query, max_tokens=500):
crew = create_crew(query)
result = crew.kickoff()
return result
# Create the Gradio interface
iface = gr.Interface(
fn=process_query,
inputs=[
gr.Textbox(lines=5, label="Enter your query"),
gr.Slider(minimum=100, maximum=1000, value=500, step=50, label="Max Tokens")
],
outputs=gr.Textbox(lines=10, label="AI Agent Response"),
title="CrewAI-powered Chatbot",
description="Enter a query and get a researched, written, and edited response using CrewAI and GROQ API."
)
iface.launch() |