app.py

import gradio as gr
from groq import Groq

# Set up the Groq client
client = Groq(api_key="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")

# Set the system prompt
system_prompt = """You are a helpful, respectful and professional assistant.
the conversation should be shorter.
Your task is to assist a marketing team in getting the budget and providing market strategies according to the budget and the platforms they're running ads on.
The platforms include Google and Meta.
You should consider the budget, the target audience, the goals of the campaign, and the strengths and weaknesses of each platform when providing market strategies.
the content should be optimized and summerized.
make the budget in Indian ruppes."""

# Initialize an empty list to store the conversation history
conversation_history = []

# Define a function to handle user messages
def handle_message(user_message,iface):
    # Add the user's message to the conversation history
    conversation_history.append({"role": "user", "content": user_message})

    # Use the Groq client to get a response from the language model
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": system_prompt,
            },
            *conversation_history
        ],
        model="llama3-8b-8192",
    )

    # Add the language model's response to the conversation history
    conversation_history.append({"role": "assistant", "content": chat_completion.choices[0].message.content})

    # Return the language model's response
    return chat_completion.choices[0].message.content

# Create a Gradio interface with a chatbot component
iface = gr.ChatInterface(handle_message)

# Launch the interface
iface.launch(share=True)