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import os
import groq
from dotenv import load_dotenv
import gradio as gr
# 1. Environment and Groq Client Setup
load_dotenv() # Load environment variables from .env
# Retrieve the API key and model identifier from environment variables
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
GROQ_MODEL_ID = os.getenv("GROQ_MODEL_ID", "llama-3.3-70b-versatile")
# Initialize the Groq client (adjust this based on your actual Groq API)
groq_client = groq.Client(api_key=GROQ_API_KEY)
# 2. Define a Simple Agent Class Using Groq
class Agent:
def __init__(self, name, role, instructions):
self.name = name
self.role = role
self.instructions = instructions
def build_prompt(self, query):
# Construct a prompt that provides context for the agent
prompt = (
f"Agent Name: {self.name}\n"
f"Role: {self.role}\n"
f"Instructions: {self.instructions}\n"
f"User Query: {query}\n"
"Response:"
)
return prompt
def respond(self, query):
prompt = self.build_prompt(query)
# Call Groq Cloud API to generate a response (adjust parameters as needed)
response = groq_client.generate(
model_id=GROQ_MODEL_ID,
prompt=prompt,
max_tokens=200
)
# Assume the API returns a dict with a 'text' field for the generated response
return response.get("text", "")
# 3. Define Specific Agents
websearch_agent = Agent(
name="WebSearch Agent",
role="Searches the web for financial information and the latest news.",
instructions="Return results with sources in APA format."
)
youtube_agent = Agent(
name="YouTube Agent",
role="Analyzes YouTube videos that are less than 22 minutes in duration.",
instructions="Provide concise and engaging answers based on the video content."
)
finance_agent = Agent(
name="Finance Agent",
role="Analyzes stock prices, analyst recommendations, and fundamentals.",
instructions="Format your response using markdown tables where applicable."
)
# 4. Build a Multi-Agent Coordinator
class MultiAgent:
def __init__(self, agents):
self.agents = agents
def handle_query(self, query):
# For the given query, ask each agent and collect their responses
responses = {}
for agent in self.agents:
responses[agent.name] = agent.respond(query)
return responses
# Create a multi-agent system with the defined agents
multi_agent = MultiAgent([websearch_agent, youtube_agent, finance_agent])
# 5. Gradio Chatbot Front End Function
def chat_response(user_query):
responses = multi_agent.handle_query(user_query)
# Format the responses into a single markdown string for display
result = ""
for agent_name, response in responses.items():
result += f"**{agent_name}**:\n{response}\n\n"
return result
# 6. Create the Gradio Interface
demo = gr.Interface(
fn=chat_response,
inputs=gr.Textbox(placeholder="Enter your message here...", lines=2),
outputs="markdown",
title="Groq Agent Chatbot",
description="A chatbot powered by Groq Cloud for GPT inference with multiple agents."
)
if __name__ == "__main__":
demo.launch()
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