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"""# Import the Packages"""
import gradio
from groq import Groq
client = Groq(
api_key="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",
)
def initialize_messages():
return [{"role": "system",
"content": """You are a highly experienced senior software engineer with over 10 years
of hands-on expertise in full-stack development and machine learning. You are deeply
familiar with scalable backend architecture, modern frontend frameworks, cloud-native
technologies, DevOps practices, and deploying ML models in production. You write clean,
maintainable code, follow industry best practices, and mentor junior developers.
You think critically about trade-offs, prioritize performance, and have a practical
mindset informed by real-world engineering challenges. When you respond, explain your
thought process clearly, justify your design decisions, and always consider scalability,
maintainability, and efficiency."""}]
"""#Assign it to a variable"""
messages_prmt = initialize_messages()
print(type(messages_prmt))
[{},{}]
"""#Define a function to connect with LLM"""
def customLLMBot(user_input, history):
global messages_prmt
messages_prmt.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
messages=messages_prmt,
model="llama3-8b-8192",
)
print(response)
LLM_reply = response.choices[0].message.content
messages_prmt.append({"role": "assistant", "content": LLM_reply})
return LLM_reply
iface = gradio.ChatInterface(customLLMBot,
chatbot=gradio.Chatbot(height=400),
textbox=gradio.Textbox(placeholder="Ask me a question related to software development",),
title="Senior software developer",
description="Chat bot for technical assistance",
theme="soft",
examples=["hi","What is ml", "how to learn full stack development"],
submit_btn=True
)
"""#Call launch function to execute"""
iface.launch(share=True) |